AI Developer / Agentic AI Engineer
Role Summary
We are building an agentic AI platform to transform commercial banking customer service. The AI developer will design, build, and operate LLM-powered agents that interpret inbound servicing requests (e.g., email / case intake), retrieve grounded knowledge, and execute approved workflows through secure tool/API integrations with enterprise-grade controls, observability, and human-in-the-loop patterns.
This role sits within a cross-functional team with Product, Operations, Technology, and Risk partners and focuses on delivering production-ready agentic AI capabilities for regulated financial services
Responsibilities
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.
Required Qualifications
- 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/Google Cloud Platform) with basic DevOps practices
- Strong understanding of security and privacy principles (PII handling, least privilege, audit logging)
Preferred Qualifications
- 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)
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