Build and enhance LLM/agent orchestration (Planner/supervisor patterns, tool-using agents, routing, guardrails)
Implement intent classification information extraction validation and decision logic for servicing workflows
Develop tool calling integrations to downstream systems (CRM, workflow engine, core banking services, case management)
Implement human-in-the-loop workflows (review, approval, escalation, override) based on confidence/risk thresholds
Design and implement retrieval-augmented generation (RAG) for policy procedure grounding and resolution guidance
Build knowledge ingestion pipelines with refresh/versioning
Improve answer quality via chunking strategies, embeddings re ranking and context management
Define and run evaluation frameworks: golden datasets, scenario tests, regression tests, and automated scoring
Reduce hallucinations and risk by implementing prompt policies, constraints, structured outputs, and verification steps
Implement observability for agents (latency, cost, tool failures, drift, quality signals, escalation rates)
Support CI/CD for agent prompts and configurations (versioning, approvals, rollback)
Collaborate with platform and security teams on secrets management, access controls, PII protections, and safe deployments

4+ years of software engineering experience or equivalent with strong CS fundamentals
Hands-on experience building with LLMs and modern AI app stack (agents, RAG, tool/function calling)
Strong proficiency in Python and building back-end services/APIs
Experience with at least one: LangChain / LangGraph, Llamalndex, Semantic Kernel or equivalent frameworks
Experience with vector databases and search (e.g., Pinecone, Weaviate, Milvus, OpenSearch/Elastic, pgvector)
Experience deploying services in cloud environments (AWS/Azure/GCP) with basic DevOps practices
Strong understanding of security and privacy principles (PII handling, least privilege, audit logging)

AI Developer

Key skills