Business Systems Analysis Analyst

2 weeks ago

Chennai, Tamil Nadu, India NTT DATA North America Full time ₹ 6,00,000 - ₹ 18,00,000 per year

LLM/agentic AI

Role Summary
You will build and support
agentic AI workflows
—LLM-powered agents that can plan multi-step tasks, use tools/APIs, retrieve knowledge, and act with guardrails. You'll work with senior engineers and SMEs to ship prototypes and production features for internal users and clients.

What You'll Do

  • Design, implement, and test agent workflows (planning, memory, tool use, retry/rollback) using frameworks such as LangGraph, LangChain, AutoGen, CrewAI or equivalent.
  • Build tooling integrations (search/RAG over vector stores like FAISS/Pinecone, REST/GraphQL APIs, databases, web/email/calendar, ticketing systems).
  • Create robust prompting (system/task prompts, tool schemas), plus evaluation harnesses (unit tests, golden sets, LLM-as-judge, offline/online evals).
  • Add guardrails for safety, PII handling, and policy compliance; instrument observability & tracing (e.g., LangSmith, OpenTelemetry logs).
  • Optimize cost/latency/reliability (caching, batching, function-calling, streaming, fallbacks).
  • Package and deploy services (Docker, basic CI/CD; cloud on AWS/Azure/GCP with secrets management).
  • Write concise tech docs and demo the work; support pilot rollouts and collect feedback.

Must-have Qualifications

  • Well trained or ~0 to 6 months hands-on experience with LLMs/agents through projects, internship, client POCs, or formal training.
  • Practical knowledge of at least one of: LangGraph/LangChain/AutoGen/CrewAI, and one of OpenAI/Anthropic/Google/Meta LLMs (function calling/tool use).
  • Strong Python (or TypeScript/Node) fundamentals; working with REST APIs, JSON, and simple data pipelines.
  • Experience implementing RAG: chunking, embeddings, vector search, relevance evaluation.
  • Understanding of prompt engineering, evaluation, and basic guardrails/safety concepts.
  • Git proficiency and clear documentation habits.

Nice to have

  • Basic frontend for agent UIs (React) or chat surfaces (Teams/Slack).
  • Cloud exposure (Azure OpenAI, AWS Bedrock, GCP Vertex), Docker, CI/CD.
  • Observability (LangSmith, Phoenix, Weights & Biases) and cost monitoring.
  • Data skills: SQL, pandas, lightweight ETL.
  • Domain exposure (e.g., finance ops, customer support, procurement) to ground tools and workflows.

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