Job highlights

- Preferred Skills Frameworks: AutoGen,CrewAI,LlamaIndex,graph-based planners,toolformer patterns Vector DBs Retrieval: Milvus,Weaviate,FAISS
- We are looking for a Senior Agentic AI Engineer to design, build, and productionize specialized agent workflows for enterprise systems
- The candidate will own end-to-end agent lifecycle management, including prompting, planning, orchestration, guardrails, tool integration, evaluation, and reliability

Initial focus will be on:
- Agent 1: Order intake discrepancy handling communications barcode Agent 2: RFS + PO creation The role requires close collaboration with business SMEs, platform teams, and other stakeholders to ensure robust, scalable, and production-ready agentic AI solutions

Key Responsibilities

Agent Development Orchestration

Implement agents using Strands Agents SDK (or equivalent) with integrations across NuOrder, Freshservice, Snowflake, MIC, RFS, POWB, GXS

Design prompts, workflows, and state machines for each sub-step, including planning, retries, and fallbacks Implement guardrails, hallucination detection, and low-confidence flows (HITL escalation, re-query, human confirmation)

Build REST APIs and lightweight SDK wrappers (Python/TypeScript) with authentication, retries, typed schemas, and OpenTelemetry context propagation

Data Reasoning Build and operate RAG pipelines, embeddings, vector search, retrieval strategies, prompt templating, and context windows

Collaborate with business SMEs to encode detection rules, templates, and PO creation logic Platform, Observability Operations

Optimize token usage, latency, throughput, and reliability Build and monitor production-grade dashboards (Grafana/Prometheus/APM) with golden signals and drill-down capabilities Integrate LangSmith, Langfuse, Phoenix, TruLens, or DeepEval for evaluation, regression suites, and dataset/version management Support feature-flag, canary, and blue-green deployments, versioned prompts/config artifacts, and SLO alerts with rollback hooks

Mandatory Skills





- Advanced Python Agent Frameworks Hands-on LLM/agent development using AWS Bedrock, LangChain/LangGraph, Strands, AutoGen, or CrewAI Agent Orchestration Reliability Planning, retries, fallbacks, guardrails, HITL escalation, multimodal inputs, deterministic schema-driven outputs
- Advanced Prompt Engineering Few-shot prompting, ReAct patterns, structured prompting, prompt decomposition, guardrails, hallucination detection RAG Expertise
- Embeddings, vector search, retrieval strategies, prompt templating; experience with vector DBs like pgvector, Pinecone, or Weaviate Production Integrations REST/GraphQL APIs, message queues (SQS/Kafka/RabbitMQ), strong error handling, idempotency Evaluation Quality Control LLM evaluation strategies, integration with LangSmith or Langfuse / Phoenix / TruLens / DeepEval Platform Deployment Hygiene Microservices (FastAPI or similar), Docker, Git, CI/CD, cloud fluency (AWS/Azure/GCP) API SDK Implementation REST endpoints, SDK wrappers, retries, auth, typed schemas, OpenTelemetry correlation Observability Monitoring Grafana/Prometheus/APM dashboards, OTEL traces, drill-down to trace level
- Quality Robustness Alerts Delta/threshold alerts, SLO breaches, on-call integrations, rollback/playbooks Business Collaboration Work with SMEs, explicit written and verbal communication
- Preferred Skills Frameworks: AutoGen, CrewAI, LlamaIndex, graph-based planners, toolformer patterns Vector DBs Retrieval: Milvus, Weaviate, FAISS; hybrid search strategies Model Ops: MLflow, experiment tracking, evaluation harnesses LLM Optimization: Context caching, quantization, memory management, prompt A/B testing
- Caching Cost Optimization: Embedding/result caching for production efficiency Vision Document Parsing: PDF/image/table ingestion, OCR, schema-based JSON outputs
- DL Stacks: PyTorch, TensorFlow, fine-tuning (LoRA/PEFT), inference optimization
- Data Engineering: PySpark, Snowflake connectors, ETL production considerations
- Security Compliance: PII handling, redaction, audit logs, approval flows Agent Interop: MCP client/server wiring, agentagent handoffs HF/PyTorch integration: Local inference tests, evaluator stubs

📌 Sr Agentic AI Engineers Experience Band (Bengaluru)
🏢 Mobile Programming
📍 Bengaluru