Job Summary
This role demand strong GenAI experience, emerging mastery in Agentic AI Systems, and a good foundation in classical ML.
You will design and build intelligent, tool-using agents, multi-agent systems, RAG pipelines, and LLM-based applications leveraging the LangChain , LangGraph ecosystem, LangSmith for evaluation.
Key Responsibilities
This role demand strong GenAI experience, emerging mastery in Agentic AI Systems, and a good foundation in classical ML.
You will design and build intelligent, tool-using agents, multi-agent systems, RAG pipelines, and LLM-based applications leveraging the LangChain , LangGraph ecosystem, LangSmith for evaluation.
Key Responsibilities
1. GenAI / LLM Application Development
- Build GenAI applications using:
- LangChain, LangGraph
- Implement RAG architectures with:
- Retrieval, reranking, chunking, memory strategies
- Vector DBs (faiss, aisearch, opensearch, PG vector etc).
- Design prompt-engineering strategies:
- Instruction-following
- ReAct (Reasoning + Acting)
- Chain-of-thought structuring
- Self-reflection and planning loops
- Evaluation Strategy
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- Implement evaluation frameworks for Classical ML and GenAI systems, covering statistical validation, reliability, and robustness.
- Assess LLM outputs, RAG pipelines, and agent workflows for grounding quality, relevance, and retrieval accuracy (e.g., recall@k, precision@k).
- Use LangSmith for tracing, automated evaluations, regression testing, and continuous system‑level quality monitoring
2. Agentic System Architecture
- Build agentic workflows:
- Tool-calling agents
- Planner–executor systems
- Multi-agent communication systems
- Hierarchical agent architectures
- Deep Agents
- Integrate memory systems:
- episodic memory
- semantic memory
- vector-based long-term knowledge
- Implement evaluation frameworks for agentic systems using LangSmith.
3. Model Context Protocol (MCP) & Tooling
- Implement MCP servers for external tool connectivity.
- Build tools that allow agents to interact with:
- APIs
- Code execution environments
- Knowledge bases
- Company applications
4. Classical ML (Foundational DS Skills)
- Apply ML models to structured/unstructured data.
- Conduct feature engineering, model selection, hyperparameter tuning.
- Build interpretable models where required.
5. Engineering & Integration
- Collaborate with backend engineering teams to seamlessly integrate agentic and GenAI systems into production applications.
- Implement observability, tracing, and monitoring for GenAI workflows using LangSmith to ensure reliability and system‑level transparency.
6. Cloud ML-Ops & Quality
- ML Modelling, data drift, concept drift, model quality monitoring.
- Hands‑on experience across AWS/ Azure/ Databricks, with flexibility to work on any cloud platform.
- Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown)
Skill Requirements
Required Skills & Experience
- 5–10 years total experience, with 2–4+ years hands-on GenAI.
- Hands-on expertise with:
- LangChain, LangGraph
- LangSmith (tracing, metrics, evaluations)
- MCP tooling and agent tool integration
- ReAct, Tree of Thoughts, multi-agent orchestration
- RAG patterns and vector databases
- Strong coding expertise in Python.
- Classical ML foundations (tree models, regression, etc.).
- Experience working with LLM APIs and/or open-source LLMs.
- Experience building and debugging production-quality GenAI pipelines.
- Aws/azure
- GIT Ops
- Prior experience building complex multi-agent systems for real-world applications.
- Knowledge of multi-modal LLMs (vision, speech, code).
- Familiarity with structured evaluation of LLM systems (hallucination tests, safety assessments etc ).
- Experience in enterprise-grade LLM deployments.
Other Requirements
BE or Equivalent degree