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Business Systems Analysis Analyst at NTT DATA
Chennai, tamil nadu, India -
Full Time

Start Date

Immediate

Expiry Date

06 Feb, 26

Salary

0.0

Posted On

08 Nov, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Business Systems Analysis, Agent Workflows, LangGraph, LangChain, AutoGen, CrewAI, Tooling Integrations, Prompt Engineering, Python, TypeScript, REST APIs, SQL, Docker, CI/CD, Observability, Data Skills

Industry

IT Services and IT Consulting

Description
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. 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. 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.
Responsibilities
Design, implement, and test agent workflows using various frameworks. Build tooling integrations and create robust prompting and evaluation harnesses.