Bengaluru, Karnataka
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
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
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