
Switch to a High Paying Data Science & ML Career Without Quitting Your Job
Switch to a High Paying ML and Gen AICareer Without Quitting Your Job
Master Python, Machine Learning, DL, Agentic AI & Gen AI for Top ML & Gen AI Roles in Just 9 Months
Structured Curriculum
1:1 Mentorship by MAANG Experts
Industry-Relevant Projects

"Got an amazing salary hike, thanks to Bosscoder Academy's Program"
- Pulkit Gupta
Maxhome.ai
















































































































































































































































99 LPA
HIGHEST PACKAGE
104%
AVERAGE SALARY HIKE
2200+
LEARNERS PLACED
SUCCESS STORIES
Bosscoder has helped build 2200+ Careers
instructors & MENTORS
Become a skilled
Data Scientist
ML Engineer
Data Analyst
Applied Scientist
Sr. Data Scientist
Average Salary Package
24-26 Lakhs
Highest Salary after Bosscoder
99 Lakhs
Real World ML & Gen AI Projects You’ll Work On
The Control Room
Build an observability and eval stack for LLM systems with tracing, dashboards, and CI-integrated evals.
The Discovery Engagement
Run a client discovery engagement that turns a vague ask into a signed, estimated project scope.
The Data Excavation
Reverse-engineer a legacy system with CDC and entity resolution to build a clean data sync pipeline.
Prompt Evaluation Lab
Evaluate CoT, self-consistency, and tree-of-thought prompting against a zero-shot baseline for cost vs quality.
Enterprise Support Assistant
Build a multi-turn support assistant with a role-contract system prompt, then red-team it against 8 attack classes.
The Model Bake-Off
Build a provider-agnostic LLM client with async streaming and caching to compare cost and latency across providers.
The Resilient Ingestor
Build a fault-tolerant async pipeline that ingests 50,000 paginated records from a flaky API with retries.
The Policy Assistant
Build a production RAG pipeline over policy documents with chunking, embeddings, and vector retrieval.
The Retrieval Bake-Off
Improve retrieval using hybrid BM25 and vector search, cross-encoder reranking, and RAGAS evaluation.
The Multi-Hop Investigator
Build a multi-hop RAG agent that decomposes questions, retrieves evidence across hops, and cites its answers.
The Specialist
Fine-tune a small open-source model with LoRA/QLoRA to beat a larger general-purpose model on a narrow task.
The Operations Agent
Build a tool-calling agent on MCP-standardized servers, gated by an arbiter layer that evaluates its actions.
The Claims Desk
Build a stateful claims agent in LangGraph with persistent memory and a human-in-the-loop approval step.
The Service
Turn a notebook prototype into a production async API service, containerized and shipped with CI/CD.
The Control Room
Build an observability and eval stack for LLM systems with tracing, dashboards, and CI-integrated evals.
The Discovery Engagement
Run a client discovery engagement that turns a vague ask into a signed, estimated project scope.
The Data Excavation
Reverse-engineer a legacy system with CDC and entity resolution to build a clean data sync pipeline.
Prompt Evaluation Lab
Evaluate CoT, self-consistency, and tree-of-thought prompting against a zero-shot baseline for cost vs quality.
Enterprise Support Assistant
Build a multi-turn support assistant with a role-contract system prompt, then red-team it against 8 attack classes.
The Model Bake-Off
Build a provider-agnostic LLM client with async streaming and caching to compare cost and latency across providers.
The Resilient Ingestor
Build a fault-tolerant async pipeline that ingests 50,000 paginated records from a flaky API with retries.
The Policy Assistant
Build a production RAG pipeline over policy documents with chunking, embeddings, and vector retrieval.
The Retrieval Bake-Off
Improve retrieval using hybrid BM25 and vector search, cross-encoder reranking, and RAGAS evaluation.
The Multi-Hop Investigator
Build a multi-hop RAG agent that decomposes questions, retrieves evidence across hops, and cites its answers.
The Specialist
Fine-tune a small open-source model with LoRA/QLoRA to beat a larger general-purpose model on a narrow task.
The Operations Agent
Build a tool-calling agent on MCP-standardized servers, gated by an arbiter layer that evaluates its actions.
The Claims Desk
Build a stateful claims agent in LangGraph with persistent memory and a human-in-the-loop approval step.
The Service
Turn a notebook prototype into a production async API service, containerized and shipped with CI/CD.
The Control Room
Build an observability and eval stack for LLM systems with tracing, dashboards, and CI-integrated evals.
The Discovery Engagement
Run a client discovery engagement that turns a vague ask into a signed, estimated project scope.
The Data Excavation
Reverse-engineer a legacy system with CDC and entity resolution to build a clean data sync pipeline.
Prompt Evaluation Lab
Evaluate CoT, self-consistency, and tree-of-thought prompting against a zero-shot baseline for cost vs quality.
Enterprise Support Assistant
Build a multi-turn support assistant with a role-contract system prompt, then red-team it against 8 attack classes.
The Model Bake-Off
Build a provider-agnostic LLM client with async streaming and caching to compare cost and latency across providers.
The Resilient Ingestor
Build a fault-tolerant async pipeline that ingests 50,000 paginated records from a flaky API with retries.
The Policy Assistant
Build a production RAG pipeline over policy documents with chunking, embeddings, and vector retrieval.
The Retrieval Bake-Off
Improve retrieval using hybrid BM25 and vector search, cross-encoder reranking, and RAGAS evaluation.
The Multi-Hop Investigator
Build a multi-hop RAG agent that decomposes questions, retrieves evidence across hops, and cites its answers.
The Specialist
Fine-tune a small open-source model with LoRA/QLoRA to beat a larger general-purpose model on a narrow task.
The Operations Agent
Build a tool-calling agent on MCP-standardized servers, gated by an arbiter layer that evaluates its actions.
The Claims Desk
Build a stateful claims agent in LangGraph with persistent memory and a human-in-the-loop approval step.
The Service
Turn a notebook prototype into a production async API service, containerized and shipped with CI/CD.
The Control Room
Build an observability and eval stack for LLM systems with tracing, dashboards, and CI-integrated evals.
The Discovery Engagement
Run a client discovery engagement that turns a vague ask into a signed, estimated project scope.
The Data Excavation
Reverse-engineer a legacy system with CDC and entity resolution to build a clean data sync pipeline.
Prompt Evaluation Lab
Evaluate CoT, self-consistency, and tree-of-thought prompting against a zero-shot baseline for cost vs quality.
Enterprise Support Assistant
Build a multi-turn support assistant with a role-contract system prompt, then red-team it against 8 attack classes.
The Model Bake-Off
Build a provider-agnostic LLM client with async streaming and caching to compare cost and latency across providers.
The Resilient Ingestor
Build a fault-tolerant async pipeline that ingests 50,000 paginated records from a flaky API with retries.
The Policy Assistant
Build a production RAG pipeline over policy documents with chunking, embeddings, and vector retrieval.
The Retrieval Bake-Off
Improve retrieval using hybrid BM25 and vector search, cross-encoder reranking, and RAGAS evaluation.
The Multi-Hop Investigator
Build a multi-hop RAG agent that decomposes questions, retrieves evidence across hops, and cites its answers.
The Specialist
Fine-tune a small open-source model with LoRA/QLoRA to beat a larger general-purpose model on a narrow task.
The Operations Agent
Build a tool-calling agent on MCP-standardized servers, gated by an arbiter layer that evaluates its actions.
The Claims Desk
Build a stateful claims agent in LangGraph with persistent memory and a human-in-the-loop approval step.
The Service
Turn a notebook prototype into a production async API service, containerized and shipped with CI/CD.
The Control Room
Build an observability and eval stack for LLM systems with tracing, dashboards, and CI-integrated evals.
The Discovery Engagement
Run a client discovery engagement that turns a vague ask into a signed, estimated project scope.
The Data Excavation
Reverse-engineer a legacy system with CDC and entity resolution to build a clean data sync pipeline.
Foundation
of Every Bosscoder Success StoryYour journey from learning to landing a ML Engineer Role role, we guide you at every step.
Learn Directly from Industry Experts & Get Real-time Guidance
Built to match real tech roles.
Rigorous interview preparation & regular contests.
Personalized placement guidance
End-to-end job prep support


Frequently Asked Questions
General Questions
What is the fee for the Advanced AI & ML Engineering Program?
The total program fee is ₹1,50,000.
We also offer flexible payment options, including installment plans, to make it easier for working professionals to enroll without financial stress.
What is this program about?
Who is this program best suited for?
Are the classes live or recorded?
Does Bosscoder provide placement support?
What kind of projects will I build during the program?
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