AI Engineering Course for DevelopersBuild Production AI Systems with RAG,
AI Agents & MCP
A live online AI engineering course for Python developers, software engineers and technical professionals who want to build, deploy and ship production AI applications with LLMs, RAG, AI agents and MCP — not just use AI tools.
- 8 modules: LLM app development, RAG, Agentic AI, MCP and production deployment
- Live code review and project feedback from a practitioner instructor
- All 5 projects deployed and published to your GitHub portfolio
- Free AI Career Strategy Call — no payment required
Free consultation · No payment required · Response within business hours
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Course Preview
Watch: What It Takes to Become an AI Engineer in 2026
Before joining an AI Engineering course, developers often ask what skills really matter beyond basic chatbot building. This short video explains why modern AI engineers need RAG, AI agents, MCP, evaluation, deployment and production thinking.
- Understand why chatbot building alone is not enough
- See the skill areas covered in this AI Engineering Course
- Learn how RAG, AI agents, MCP and deployment fit into the roadmap
- Decide whether this course matches your current Python/software background
What Is an AI Engineering Course?
An AI engineering course teaches developers and technical professionals how to build AI-powered software applications using large language models (LLMs), retrieval-augmented generation (RAG), AI agents, evaluation workflows, APIs and production deployment practices. This is an AI development course focused on application engineering — not an AI awareness workshop or a data science programme.
A well-structured AI engineering training programme covers the full stack from LLM API integration through to cloud deployment: prompt engineering for developers, embedding models, vector databases, RAG pipelines, agentic workflows, MCP connectivity, evaluation frameworks, and production observability. Think of it as an AI programming course for Python developers who already know how to build software but need to add AI capabilities.
This live online programme is specifically an AI course for software developers — backend engineers, full-stack developers, Python developers, and data professionals making the move into LLM application engineering. It is a structured AI engineer learning path from foundations to production deployment, covering the tools and patterns that engineering teams are actually using in 2026.
Python Prerequisite: Intermediate Level
You should be comfortable writing functions, working with dictionaries and lists, and calling REST APIs in Python. No machine learning or data science background is required — this is an AI engineering course for developers, not data scientists. New to Python? Complete a free Python basics course first; this programme is not suitable for complete programming beginners.
2026 AI Skills Market
Why AI Engineering Is One of the Fastest-Growing Technical Skills in 2026
The gap between developers who use AI tools and engineers who build production AI applications is widening — and so is the difference in opportunity. This generative AI engineering course is designed for developers who want to close that gap with a structured AI engineer learning path.
RAG
↑ High Demand
Agentic AI
↑ Fastest Growing
MCP
↑ Emerging Standard
LLM Engineering
↑ Core Dev Skill
AI Deployment
↑ Critical Skill
AI Automation
↑ Enterprise Ready
Prompt Engineering vs AI Engineering
| Prompt Engineering | AI Engineering ← this course |
|---|---|
| Write better prompts | Design full AI application architectures |
| Use existing AI products | Build and deploy your own AI products |
| Single-turn interactions | Multi-step agentic AI workflows |
| ChatGPT / Copilot interface | LangGraph, RAG pipelines, MCP servers |
| No deployment required | FastAPI, Docker, cloud deployment |
| Commodity skill — 2023 era | Engineering skill — 2025–2026 demand |
AI Engineering Course vs Prompt Engineering vs Data Science
Three different skill sets are often confused. Here is how this AI engineering course compares to prompt engineering training and traditional data science programmes.
| Focus | Prompt Engineering | Data Science | AI Engineering ← this course |
|---|---|---|---|
| What you build | Better prompts | Models and analysis | Deployed AI applications |
| Core skill | Prompt wording and iteration | Statistics, ML, pandas | Software engineering + LLM systems |
| Typical output | A prompt template | A trained model or report | A production RAG/agent API |
| Deployment involved | No | Sometimes | Yes — FastAPI, Docker, cloud |
| Best next role | AI power user | ML Engineer / Data Scientist | AI Engineer / LLM App Developer |
If you already know Python and want to build and deploy real AI-powered applications — not just prompt existing tools or train models — this AI engineering course is the closest match.
Your Instructor
Learn from a Practitioner Who Builds Production AI
Pankaj Rana
Founder & Lead Instructor
AI practitioner and training specialist with over a decade building and shipping production software and AI systems. Has personally trained more than 1,500 professionals across engineering, banking, IT services, and manufacturing — from junior developers to CTOs. Every cohort of this live AI engineering course includes direct code review and hands-on project guidance.
Why Developers Get Stuck at the AI Surface Level
Using ChatGPT every day is not the same as being an AI engineer. Here is where most Python developers and software engineers hit a wall.
I use ChatGPT but cannot build AI applications
Prompting and AI application development are different disciplines. This AI programming course closes that gap with real system-building from day one.
I know Python but not RAG pipelines
Connecting Python to vector databases, embeddings and retrieval systems is a specific skill set we cover from the ground up in this AI course for Python developers.
Too many frameworks — I don't know where to start
LangChain, LangGraph, CrewAI, MCP... we cut through the noise and focus on what actually matters in production AI development.
I can prototype but cannot deploy production AI
Moving from Jupyter notebook to a live API is a completely different challenge. Module 8 focuses entirely on production AI deployment for this AI development course.
I want AI skills that are useful at work today
Every module in this live AI engineering course is built around use cases that engineering teams are already implementing in real organisations.
I need portfolio projects, not just certificates
Five production-grade projects, all deployed, all on GitHub. The kind of evidence a hiring manager or CTO actually examines.
Is This AI Engineering Course Right for You?
Good fit if you
- Know Python at an intermediate level
- Want to build and deploy AI applications, not just use AI tools
- Prefer a structured, live cohort with peers and a fixed schedule
- Are an individual developer, not enrolling on behalf of a team
- Want a portfolio of deployed projects, not just a certificate
Look elsewhere in our catalogue if you
- Are a complete beginner to programming
- Need to train an engineering team of 8+ — see Production AI Engineering for teams
- Want a fully personalised, 1:1 paced programme — see 1:1 AI Engineering Mentorship
- Want non-technical, business-focused AI training
Not sure if this AI Engineering programme is right for you?
Book a free call. Our instructor will review your background and tell you honestly whether this live online AI engineering course matches your goals.
Programme Outcomes
What You Will Be Able To Build
After completing this AI application development course, you will have the engineering skills to design, build and deploy these systems independently.
Enterprise RAG Systems
Knowledge bases that retrieve from internal documents, PDFs, databases and APIs in real time.
AI Agents
Autonomous agents that plan multi-step tasks, call tools, and execute workflows without human intervention.
MCP Integrations
AI assistants connected to external systems via Model Context Protocol — databases, SaaS tools, REST APIs.
AI APIs
Production-ready AI backends served via FastAPI, containerised with Docker, deployed on cloud infrastructure.
AI Automation Workflows
Multi-agent automation pipelines using LangGraph and CrewAI — research, writing, data processing, reporting.
Production AI Deployments
End-to-end deployments with monitoring, cost control, safety guardrails and CI/CD pipelines.
GitHub Portfolio
AI Engineering Course Projects: 5 Production Systems You Ship
Not tutorial copies. Not toy demos. Real architectures, real APIs, real deployments — all published to your GitHub portfolio. This production AI course is built around shipping real systems.
RAG-Powered Knowledge Assistant
A production knowledge base that answers questions from your documents. PDF/CSV/web ingestion pipeline, semantic search, re-ranking, and REST API endpoint.
Multi-Agent Research & Analysis Workflow
An autonomous multi-agent pipeline that plans, researches external sources, synthesises findings, and produces structured reports — with human-in-the-loop checkpoints.
MCP-Connected AI Assistant
An AI assistant connected to live external data sources via Model Context Protocol — reads databases, calls APIs, accesses files, all through the MCP standard.
Customer Support AI with Guardrails
A production-grade support agent with conversation memory, content safety filtering, response quality checks, and latency-optimised architecture.
Deployed AI API — FastAPI + Docker + Cloud
A complete production deployment: containerised AI API, cloud infrastructure, CI/CD pipeline, health checks, cost monitoring, and performance logging.
8 Modules
AI Engineering Training Curriculum: LLM Apps, RAG, Agentic AI, MCP and Deployment
From LLM fundamentals to production AI deployment — a structured AI engineer learning path designed for working professionals. This LLM engineering course covers the full production stack.
What You Build in the RAG and Agentic AI Course Modules
Module 1: LLM Foundations and Prompt Engineering
- GPT-4, Claude and Gemini API integration
- Prompt engineering patterns and structured outputs
- Token management and context window optimisation
- Programmatic LLM response evaluation
Module 2: RAG Pipelines and Vector Databases
- Embeddings and semantic search fundamentals
- ChromaDB, Pinecone and FAISS for vector storage
- Document chunking, ingestion and retrieval strategies
- RAG evaluation, re-ranking and hallucination reduction
Module 3: LangChain and LlamaIndex
- LangChain chains, memory and callbacks
- LlamaIndex for document indexing and querying
- Custom tools and LangSmith observability
- Building end-to-end LangChain pipelines
Module 4: Model Context Protocol (MCP)
- MCP specification and architecture overview
- Building MCP tool servers and resource providers
- Integrating external data sources via MCP
- MCP with Claude, GPT-4 and open-source models
Module 5: LangGraph, CrewAI and Agentic AI
- Graph state machines for agent orchestration
- Multi-agent patterns and parallel task execution
- CrewAI for role-based multi-agent systems
- Human-in-the-loop, supervision and safety layers
Module 6: AI Deployment with FastAPI, Docker and Cloud
- Serving AI models as REST APIs using FastAPI
- Containerising AI applications with Docker
- Deploying on AWS Cloud Run, GCP or Azure
- CI/CD pipelines and production-ready architecture
Module 7: Monitoring, Evaluation and Cost Optimisation
- LangSmith for tracing and observability
- Latency profiling and response quality evaluation
- Cost management across model providers
- Guardrails, content filtering and safety evaluation
Module 8: Capstone Project and Portfolio Review
- End-to-end production AI system build
- GitHub portfolio preparation and documentation
- Code review and mentor feedback session
- Certificate of completion from Technovids
New to RAG? Read our complete What is RAG guide — covers architecture, vector databases, chunking, and production RAG system design.
New to Agentic AI? Read our Agentic AI guide — covers architecture, tools, multi-agent systems and enterprise use cases.
New to MCP? Read our complete MCP guide — covers Model Context Protocol architecture, servers, clients, use cases and security.
Need LLMOps context? Read our LLMOps guide and how to evaluate LLM applications.
New to AI engineering? Start with our complete AI Engineering Guide — covers what AI engineering is, skills, tools, career paths and how it all fits together.
This Programme vs Random YouTube Learning
Free tutorials teach you syntax. This live AI engineering course teaches you how to ship.
| Feature | YouTube / Udemy | This Programme |
|---|---|---|
| Structured, sequenced curriculum | ||
| Live instructor — ask questions | ||
| Code review on your projects | ||
| Production deployment covered | ||
| Real portfolio projects on GitHub | ||
| MCP and Agentic AI in depth | ||
| Career guidance and strategy call | ||
| Cohort peers and support group | ||
| Watch at 1.5× speed and forget |
Career Paths After AI Engineering Training
Developers and data professionals who complete this programme move into roles that require hands-on AI system-building skills. No job placement guarantee — career outcomes depend on prior experience, project work, and interview preparation.
AI Engineer
₹10–25 LPA
Generative AI Developer
₹12–28 LPA
LLM Application Developer
₹10–22 LPA
RAG Engineer
₹12–28 LPA
AI Automation Engineer
₹8–18 LPA
AI Technical Consultant
₹15–35 LPA
Salary ranges are indicative based on publicly available job postings in India. Actual compensation varies by employer, prior experience, location and role. Technovids does not guarantee specific job placements or salary outcomes.
Which Technovids AI Engineering Programme Should You Choose?
Technovids runs three AI engineering programmes. Here is how to pick the right one in a few seconds.
This page
AI Engineering Course
You are an individual developer who wants a structured live cohort with peers, on a fixed weekend schedule, at cohort pricing.
You are hereFor teams
Production AI Engineering
You are hiring on behalf of an engineering team of 8–20 that needs a customised corporate programme, team pricing, and L&D documentation.
View corporate programme →For individuals
1:1 AI Engineering Mentorship
You want a fully personalised roadmap, your own schedule, and 1:1 code review — and can invest in premium pricing for it.
View mentorship programme →AI Engineering Course with Certificate of Completion
On completing the programme and submitting your five projects, you receive a verifiable certificate of completion from Technovids Consulting Pvt. Ltd., shareable directly on LinkedIn as a credential.
What is included
- Certificate issued by Technovids Consulting Pvt. Ltd.
- Verifiable and shareable on LinkedIn
- Confirms 80-hour programme completion
- Confirms submission of all 5 production projects
What to know
- Not a government-accredited certification
- Not a vendor certification from OpenAI or Anthropic
- Not a university-awarded qualification
- GitHub portfolio typically provides stronger practical evidence
For learners searching for AI engineering certification: this programme provides a Technovids certificate of completion rather than an independent government, university or vendor certification. Your five deployed GitHub projects typically demonstrate practical competence more effectively than any certificate alone.
AI Engineering Course vs 1:1 AI Engineering Mentorship
Both options lead to production AI engineering skills — the right choice depends on your learning style, schedule flexibility, and budget.
Live Public Cohort
This page
- Shared structured batch with peers
- Fixed weekend schedule
- Standard curriculum path
- Group Q&A and cohort support
- Cohort pricing
- 5 production portfolio projects
1:1 AI Mentorship
Personalised
- Personalised sessions, your schedule
- Custom learning roadmap
- Individual code review every session
- Direct 1:1 instructor guidance
- Premium pricing
- Role-specific project guidance
| Live Public Cohort (this page) | 1:1 Mentorship | |
|---|---|---|
| Format | Shared structured batch | Personalised schedule |
| Learning style | Peer learning | Individual instruction |
| Curriculum | Standard learning path | Custom learning roadmap |
| Pricing | Cohort pricing | Premium fee |
| Q&A | Group live Q&A | Personal code review |
| Schedule | Fixed weekend schedule | Flexible — your hours |
| Best for | Structured learning | Personalised acceleration |
Prefer personal instruction, a custom roadmap, and individual code review? Explore our 1:1 AI engineering mentorship.
A Live AI Engineering Course Online for Developers
Not a video course. Not self-paced watching. A structured public cohort with live instruction, direct project feedback, and a cohort of developers learning together. This is an AI engineering course online that runs live — with real humans, real Q&A, and real code review.
Live Weekend Sessions
Live online sessions every Saturday and Sunday via Zoom or Google Meet — with direct Q&A and real-time code walkthroughs.
Session Recordings
Every session is recorded and available within 24 hours so working professionals can catch up on any session they miss.
Project Assignments
Hands-on project work between sessions — building real production AI systems, not completing quizzes.
Code & Project Review
Your actual project code is reviewed by the instructor. Specific, actionable feedback on your RAG and agent implementations.
Cohort Support Group
Dedicated cohort WhatsApp group for questions, peer discussion and async support between sessions.
Certificate on Completion
Verifiable certificate from Technovids Consulting Pvt. Ltd., shareable on LinkedIn.
AI Engineering Course: Quick Facts
| Duration | 80 hours — 8 structured modules |
| Format | Live instructor-led, online (Zoom / Google Meet) |
| Schedule | Weekend batches — Saturday & Sunday sessions |
| Audience | Python developers, software engineers, data professionals, technical career switchers |
| Prerequisite | Python intermediate — no ML or data science background needed |
| Projects | 5 production GitHub projects (RAG, AI agents, MCP, deployed AI API) |
| Certificate | Verifiable completion certificate — Technovids Consulting Pvt. Ltd., shareable on LinkedIn |
| Next batch | Request next cohort date — contact us or WhatsApp +91 86183 46384 |
| Fee | Request fee details — varies by batch and early-bird period |
| Instructor | Pankaj Rana, Founder & Lead Instructor, Technovids |
| Delivery | Online — India-focused, IST-compatible time zones |
| Frameworks covered | LangChain, LangGraph, CrewAI, LlamaIndex, MCP, FastAPI, Docker |
Who Should Take This AI Engineering Course Online?
This programme is built for technical professionals who write code. If you are a developer, data professional, or technical career switcher looking for a structured AI engineer learning path — not just AI tool training — this is designed for you.
Software Developers
Perfect fitBackend, full-stack, and DevOps engineers adding production AI capabilities to their skillset and portfolio.
Python Developers
Perfect fitPython engineers building their first AI-native products — RAG pipelines, LangGraph agents, and deployed LLM APIs. This is an AI course for Python developers by design.
Data Engineers
Strong fitData engineers moving into LLM system building, vector databases, and AI-powered data pipelines.
Data Scientists
Strong fitData scientists who can build models but want to close the gap to production LLM application engineering and deployment.
Technical Career Switchers
Good fitProfessionals with Python experience switching into AI engineering roles. This AI development course provides the structured path.
Technical Managers
Good fitEngineering leads who write code and need depth in AI application development to effectively guide their teams.
Prerequisites
Not comfortable with Python yet? This AI programming course assumes you can read and write basic Python. We recommend completing a free Python basics course before joining. This programme is not suitable for complete programming beginners.
AI Engineer Learning Path
This programme follows a structured AI engineer learning path from foundations to production deployment. Here is the progression covered across 8 modules.
LLM APIs & Prompt Engineering for Developers
Embeddings, Vector Search & RAG Foundations
Advanced RAG Systems
Agentic AI & Tool Use
Multi-Agent Systems (LangGraph & CrewAI)
MCP & Connected AI Applications
Evaluation & Hallucination Control
Production Deployment & Observability
Capstone Production AI Project
Portfolio, Career Strategy & Next Steps
Industry Use Cases Covered in This AI Engineering Course
Projects and examples in this programme are drawn from real enterprise AI application deployments across six industries where AI engineering skills are in highest demand.
BFSI
Banking, Financial Services & Insurance
- ·Loan document Q&A (RAG)
- ·Compliance policy assistant
- ·Fraud pattern detection agent
Healthcare & Pharma
Hospitals, Clinical Research & Pharmaceuticals
- ·Clinical notes summarisation
- ·Drug interaction knowledge assistant
- ·Medical report RAG pipeline
IT Services
Software, BPO & IT Consulting
- ·Code review agent
- ·Internal knowledge base assistant
- ·Support ticket triage automation
Manufacturing
Engineering, FMCG & Supply Chain
- ·Equipment maintenance assistant
- ·SOP document Q&A
- ·Supply chain anomaly agent
Training & Education
EdTech, Corporate L&D & Universities
- ·Personalised learning assistant
- ·Curriculum Q&A bot
- ·Assessment feedback agent
Retail & E-commerce
Retail, D2C & Consumer Tech
- ·Product catalogue assistant
- ·Customer support RAG
- ·Returns policy agent
Training a Developer Team?
This page is designed for individual learners joining a live public AI engineering cohort. For corporate teams that need a customised programme, dedicated scheduling, team-size pricing and L&D documentation, see our Production AI Engineering training for companies. For broader team AI upskilling across functions, see Corporate AI Training Programs.
Free Resource
Get the 2026 AI Engineering Career Roadmap
A practical PDF covering the tools, frameworks, learning path and career progression for AI engineers in India in 2026 — including the full AI engineer learning path from Python basics to production deployment.
Send me the 2026 Roadmap
Delivered to your inbox. Free.
What Learners Say About Technovids AI Training
Feedback from developers who have completed our AI Engineering training.
“Before joining Technovids, I had watched many AI tutorials online but struggled to connect the concepts. This course gave me a proper learning path. The trainer explained topics patiently and used practical examples during the live classes. The assignments also helped me understand where I needed more practice.”
Rohan Kulkarni
Pune
“I come from a non-AI background, so I was initially worried that the course might be too difficult. However, the sessions started with the fundamentals and gradually moved towards advanced topics. I could ask questions freely, and the doubt-clearing support was genuinely helpful.”
Aditi Sharma
Delhi
“The best part of the AI Engineering course was the hands-on learning. We did not just study concepts—we also worked on practical exercises and projects. Building things myself gave me much more confidence than simply watching recorded tutorials.”
Sai Kiran Reddy
Hyderabad
“As a college student, I needed a course that I could manage alongside my regular classes. The online format worked well for me, and having access to session recordings made revision easier. The trainer kept the classes interactive, so they never felt like one-way lectures.”
Neha Iyer
Chennai
“I already knew basic Python but had very little understanding of how AI applications are actually developed. This course helped me connect programming with real AI use cases. By the end, I felt more comfortable discussing projects and explaining the work I had done.”
Abhishek Jain
Jaipur
“What I appreciated most was the trainer's teaching style. Complex topics were broken down into smaller, understandable steps, and questions were encouraged throughout the class. The course requires regular practice, but the guidance from the Technovids team made the learning process much smoother.”
Kavya Nair
Kochi
“I joined the course to improve my practical skills and prepare for opportunities in AI. The structured lessons, assignments, and project guidance helped me identify my weak areas and improve them. It was a useful learning experience, especially for someone looking for direction rather than random online content.”
Manish Patel
Ahmedabad
Read more verified Technovids client case studies, or WhatsApp us to be connected with a recent graduate directly.
Training in Action
Training Sessions by Technovids
A glimpse of Technovids instructor-led training sessions across technology, AI, data and professional learning programmes.
Corporate Learning Session
Professionals gather after completing a Technovids training session.
Professional Training Programme
A team wraps up a hands-on software training session.
Practical Classroom Training Session
Participants take notes during a structured classroom session.
AI Engineering Career Opportunities
The skills covered in this course map to several roles engineering teams are actively hiring for.
AI Engineer
Generative AI Engineer
LLM Application Developer
RAG Engineer
AI Automation Engineer
AI Solutions Engineer
LangChain / LangGraph Developer
AI Product Engineer
AI Consultant
This course does not guarantee job placement. Outcomes depend on prior experience, portfolio quality, interview performance and market demand.
AI Engineering Salary and Pay-Scale Guidance
We don't publish fixed salary figures here — they date quickly and vary widely. This is general, experience-based guidance instead.
Early Career (0–2 yrs)
Typically joins as part of a larger engineering team, working on defined RAG or agent features under senior review. Portfolio project experience is a strong differentiator at this stage.
Mid-Level (2–6 yrs)
Usually owns AI features end-to-end — retrieval design, agent workflows, evaluation and deployment — with less day-to-day supervision. Prior software experience compounds with AI skills here.
Senior / Lead (6+ yrs)
Typically responsible for AI system architecture, tooling and framework decisions, and mentoring — reflecting both engineering seniority and a demonstrated AI delivery track record.
For current market ranges, we recommend checking live sources such as Glassdoor, Levels.fyi or AmbitionBox rather than relying on static figures. Salary varies by experience, company, location, portfolio quality and interview performance. Technovids does not guarantee salary outcomes.
Frequently Asked Questions
More questions? WhatsApp us directly. Or contact us here.
What is an AI engineering course?
An AI engineering course teaches developers and technical professionals how to build AI-powered software applications using large language models, retrieval-augmented generation, AI agents, evaluation workflows, APIs and deployment practices. Unlike a general AI awareness course, AI engineering training focuses on application development, system design, reliability and production readiness.
Is this AI engineering course online?
Yes. This is a live online AI engineering course delivered via Zoom or Google Meet. Sessions run on weekends to suit working professionals. All sessions are recorded and available within 24 hours for replay.
Who should join this AI engineering training?
This programme is designed for Python developers, software engineers, backend and full-stack developers, data scientists moving into LLM applications, data engineers, ML engineers, automation developers, technical founders, and final-year students with Python experience. You need basic Python proficiency — no machine learning background is required.
Do I need Python before joining?
Yes. Python basics are required. You should be able to write functions, work with APIs, handle dictionaries and lists, and install packages. If you can call a REST API in Python, you are ready. If you are new to Python, complete a free Python basics course first. This programme is not suitable for complete programming beginners.
Is this course suitable for software developers?
Yes. This is specifically an AI course for software developers. Backend engineers, full-stack developers, Python developers, and DevOps engineers are the core audience. The curriculum focuses on AI application development, system design and production deployment — not data science or model training.
What projects are included in the AI engineering course?
Five production-grade GitHub projects: (1) RAG-Powered Knowledge Assistant — semantic search over your documents; (2) Multi-Agent Research & Analysis Workflow — autonomous multi-agent pipeline; (3) MCP-Connected AI Assistant — AI connected to live data via Model Context Protocol; (4) Customer Support AI with Guardrails — conversation memory and safety filtering; (5) Deployed AI API — FastAPI, Docker, and cloud deployment with monitoring. All projects are built and deployed during the programme.
Does the course cover RAG and Agentic AI?
Yes. The programme includes a dedicated RAG and Agentic AI course module covering retrieval-augmented generation, vector search, agent workflows, tool use and practical implementation patterns. RAG coverage includes chunking, embeddings, vector databases, semantic search, hybrid search, re-ranking, and production RAG evaluation. Agentic AI coverage includes tool calling, LangGraph stateful agents, multi-agent systems with CrewAI, and human-in-the-loop workflows.
Does the course cover LLM engineering?
Yes. This is fundamentally an LLM engineering course for developers. It covers LLM API integration across OpenAI, Anthropic, and Google; prompt engineering for developers; embedding models; LangChain and LlamaIndex frameworks; production evaluation with LangSmith; cost management; and hallucination reduction — all focused on building reliable production LLM applications.
Does the course include MCP?
Yes. Model Context Protocol (MCP) is covered in depth. You will learn MCP architecture, build MCP servers that connect AI assistants to databases and APIs, and complete a full MCP-connected AI application as one of your five portfolio projects. MCP is becoming the industry standard for enterprise AI connectivity.
Is this an AI engineering certification?
For learners searching for AI engineering certification, this programme provides a Technovids certificate of completion — not an independent government, university or vendor certification. The certificate is issued by Technovids Consulting Pvt. Ltd. and is shareable on LinkedIn. Your GitHub portfolio of five production projects typically provides stronger evidence of practical competence.
What certificate will I receive?
A verifiable certificate of completion from Technovids Consulting Pvt. Ltd., shareable on LinkedIn. The certificate confirms completion of the AI Engineering Course curriculum and five production projects. It is not a vendor certification from OpenAI or Anthropic, and it is not a government-accredited credential.
How is this different from the 1:1 AI Engineering Mentorship?
This is a live public cohort programme — you join a structured batch with other developers, following a set curriculum on a shared schedule. The 1:1 AI Engineering Mentorship is personalised: individual sessions on your own schedule, a custom learning roadmap, personal code review, and premium pricing. Choose this course for structured group learning at cohort pricing. Choose the mentorship if you need a personalised learning path and individual attention.
How is this different from the Production AI Engineering corporate programme?
This page is for individual learners joining a public cohort. The Production AI Engineering programme is a corporate team training for developer teams of 8–20, delivered as a 5-day on-site intensive or 8-week online cohort, with custom curriculum, team pricing, GST invoicing, and L&D documentation. If you are an individual developer, this page is right for you. If you need to upskill your engineering team, see the corporate programme.
What is the duration and schedule?
The programme is 80 hours total across 8 modules. Sessions run live online on weekends — Saturday and Sunday — designed for working professionals. Expect 8–10 hours per week including live sessions and project work. Contact us for current batch dates and session timings.
What are the fees?
Fees depend on the batch and enrolment timing. We do not publish fees publicly as they may vary by batch and early-bird periods. Book a free AI Career Strategy Call or WhatsApp us to get current fee details, available discounts, and payment plan options.
Can I speak to someone before joining?
Yes. Book a free AI Career Strategy Call — our instructor will review your background, discuss your goals, and tell you honestly whether this programme is the right fit. No payment required and no pressure to enrol. You can also WhatsApp us at +91 86183 46384.
Free · No Commitment · No Payment Required
Ready to Move from
AI User to AI Engineer?
Book a free AI Career Strategy Call. Our instructor will review your background, discuss your goals, and help you decide if this live online AI engineering course is the right next step.
Or call +91 86183 46384Based in Bangalore? Explore our AI Engineering Course in Bangalore — blended learning with optional classroom sessions at HSR Layout.
Technovids Consulting Pvt. Ltd. · HSR Layout, Bengaluru · corporate@technovids.com