AI developer

Agentic AI Solution Development

• 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
• Developed 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

Knowledge and grounding (RAG)

• 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

Quality, Safety and Evaluation

• 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.
• Partner with risk slash compliance to ensure traceability, audit logs, explain ability requirements are met.

Production Readiness and Operations

• 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), * Experience in financial services or other regulated domains (risk controls, compliance audit readiness)
• Experience integrating with enterprise workflows (e.g., ServiceNow, Custom workflow engines, BPM/RPA)
• Familiarity with model evaluation approaches (LLM-as-judge, rubric scoring, retrieval evals, offline/online testing)
• Experience with messaging/eventing (Kafka/SQS), email ingestion pipelines, and document processing
• Exposure to MRM concerns and governance (model cards, risk assessments, validation processes)