Job Description

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:
• Implement RAG architectures with:
• Design prompt-engineering strategies:
• 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

1. Agentic System Architecture

• Build agentic workflows:
• Integrate memory systems:
• Implement evaluation frameworks for agentic systems using LangSmith.

1. Model Context Protocol (MCP) & Tooling

• Implement MCP servers for external tool connectivity.
• Build tools that allow agents to interact with:

1. Classical ML (Foundational DS Skills)

• Apply ML models to structured/unstructured data.
• Conduct feature engineering, model selection, hyperparameter tuning.
• Build interpretable models where required.

1. 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.

1. 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:
• 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