Senior AI Engineer
Skills
About This Role
Role Overview
The Senior AI Engineer leads the technical delivery of one or more AWA use cases end-to-end, from Agent Profile definition through to production UAT sign-off.
You own the architectural decisions within your use case domain: agent graph topology, confidence calibration, HITL threshold negotiation with the business, and the regression testing regime.
You mentor AI Engineers and are the primary escalation point for complex LangGraph, LLM, and evaluation challenges.
You are expected to identify failure modes before they reach production and to drive the adoption of new AWA capabilities across the use case portfolio.
Required Skills and Experience
- Technical — Essential
- 7+ years Python development; 2+ years in production AI/ML engineering
- Deep LangGraph or equivalent agentic framework experience: graph topology, state management, conditional edges, checkpoint-based recovery
- LLM evaluation expertise: F1 measurement against ground truth, Expected Calibration Error (ECE), hallucination detection, confidence calibration
- Prompt engineering at scale: versioned prompts, sensitivity analysis, few-shot dataset curation, PMS governance
- RAG architecture: hybrid retrieval (BM25 + vector), re-ranking, chunk citation, grounding verification
- Azure AI Foundry or equivalent LLM inference platform: PTU, PAYG, token budget management, APIM integration
- Workflow orchestration: Orkes Conductor or Temporal — HITL wait tasks, retry policies, compensating transactions
- CI/CD pipeline integration for ML: Azure DevOps, MLflow experiment tracking, model registry promotion
- Technical — Advantageous
- Computer vision model deployment and evaluation (object detection — TPR/TNR/IoU)
- Azure AI Document Intelligence: form recogniser, custom models, layout analysis
- Kubernetes / AKS: workload identity, namespace isolation, KEDA autoscaling
- Apache Iceberg, DuckDB, and data contract patterns
- Banking domain knowledge: cheque clearing, trade finance, KYC, AML
Qualifications
- Bachelor's degree in Computer Science, AI/ML, or equivalent; Master's preferred
- Azure AI Engineer Associate or equivalent certification
- Demonstrable production AI deployments — case studies or public work highly valued
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