Senior AI Engineer

Job Description Roles & Responsibilities Start Date ASAP My client is a global data, AI, digital transformation, and consulting company that helps organisations use data and technology to improve business performance, customer experience, and decision-making. 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. Key Responsibilities: Use case technical ownership: Own the technical delivery of one or more AWA use cases from discovery through production; define the Agent Profile, Use Case Manifest, Data Manifest, and Tool Authorisation Matrix for each use case. Architecture and design: Design LangGraph agent graph topologies for complex multi-agent pipelines including Supervisor-Worker fan-out, parallel execution, state compression, and cross-document reconciliation (3-way match). Confidence calibration and HITL thresholds: Lead the AI Metric Baselines calibration process: measure ECE, derive HITL routing thresholds empirically, present threshold recommendations to business and risk teams, and own the signed AI Metric Baselines table. Test strategy ownership: Own and execute the full AWA IDP Test Strategy for your use cases: all four testing bands (Model/Prompt/Agent/Security, Adversarial, Integration, UAT) through to sign-off. Agent Regression Testing: Maintain the Golden Dataset and Regression Dataset for your use cases; configure CI/CD regression pipeline gates; review regression results after every model or prompt change. Mentoring and code review: Mentor AI Engineers; conduct substantive technical code reviews of agent graphs, prompts, and MCP tool implementations; maintain AWA coding standards. Stakeholder engagement: Present model quality metrics, STP rates, and HITL rates to Ops leads and business owners; recommend threshold adjustments with data-backed rationale. New capability adoption: Evaluate and prototype new AI capabilities (new Azure AI Foundry models, new LangGraph features, new RAG patterns) for incorporation into the AWA accelerator catalogue. Required Skills and Experience: Technical Essential: 5+ 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 Salt is acting as an Employment Business in relation to this vacancy. Desired Candidate Profile 5+ 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 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 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 Company Industry ConsultingManagement ConsultingAdvisory Services Department / Functional Area Engineering Keywords Senior AI Engineer Get real-time job updates only on our App