Design, build, and operate production-grade agentic AI systems in Python using modern agent frameworks. You will implement complete agentic workflows including planning, tool use, multi-agent coordination, retrieval (RAG), evaluation, safety controls, and observability. The role requires strong engineering fundamentals, hands-on experience shipping agentic systems, and the ability to continuously improve reliability and quality using evaluation-driven iteration.

Responsibilities:-

- Build and operate agentic workflows in Python using LangGraph and/or Semantic Kernel (LangChain/AutoGen/CrevAI as additional exposure).
- Implement robust tool/function calling: schema validation, structured arguments/outputs, safe tool execution, retries, idempotency patterns, and permission-scoped tool access.
- Develop and optimize RAG / Agentic RAG pipelines: ingestion, chunking, embeddings, hybrid retrieval, reranking, query planning, grounding, caching, and citation/trace strategies.
- Design multi-agent systems: coordinator-worker, planner-executor, task decomposition, delegation, and optional human-in-the-loop checkpoints.
- Drive evaluation and quality systems: regression suites, grounding/faithfulness checks, hallucination detection, task success metrics, latency/cost monitoring, and release gates.
- Use cloud AI platform services (AWS preferred) for model access/orchestration and embeddings, and integrate these into enterprise-grade agent systems.
- Ensure production readiness: observability (logs/metrics/traces), dashboards and alerting, and operational runbooks for common failure modes.
- Apply security best practices for LLM/agentic systems: secrets handling, least privilege, prompt-injection defenses, content safeguards, audit logging, and protected integration boundaries.
- Collaborate with architects, backend/platform engineers,



and product teams to improve reliability, performance, cost, and UX.

Must Have:-

- Strong Python engineering: async patterns, clean modular code, testing (pytest), profiling/performance debugging.
- Hands-on experience delivering production agentic systems using LangGraph and/or Semantic Kernel, including tool orchestration and multi-step workflows.
- Deep understanding of RAG systems end-to-end: ingestion retrieval grounding, including hybrid search and retrieval optimization concepts.
- Multi-agent patterns and protocols: coordinator-worker, planner-executor, task decomposition; awareness of MCP and A2A-style interoperability patterns.
- Evaluation and quality depth: experience with promptfoo, Phoenix/Arize (or equivalent), custom eval pipelines.

Cloud Platform AI Services Experience (AWS Preferred)

- Using managed model/embedding services (e.g., AWS Bedrock or equivalents) and integrating them into production systems.

Practical Production Engineering Fundamentals
- API design, auth/authz concepts, rate limiting, retries, idempotency, and resilient service patterns.

Experience Deploying Services
- Using Docker and CI/CD pipelines; ability to work with cloud runtime environments.

Security Awareness for GenAI
- Prompt injection defense, safe tool execution boundaries, secrets management, audit logging.

Good to Have:-

- AWS infrastructure depth: EKS/ECS, S3, RDS, ElastiCache (Redis), SQS/SNS, API Gateway, OpenSearch / OpenSearch Serverless, Secrets Manager.
- Observability tooling depth: Datadog, OpenTelemetry, CloudWatch, distributed tracing, SLO-style alerting.
- Enterprise integrations exposure: SAP / Salesforce / ServiceNow (or similar), API governance, throttling patterns.
- Streaming/chat-based UX patterns: Trace visibility (server-sent events, token streaming).
- Strong system design: Scalable architectures, failure mode analysis, cost/performance tradeoffs.

📌 Agentic AI Engineer (Bengaluru)
🏢 EY
📍 Bengaluru