AI/ML/DevOps Engineer

Job Description Roles & Responsibilities What you'll own: Own the end-to-end MLOps/LLMOps reference architecture: ingestion validation feature and embedding pipelines training and fine-tuning evaluation registry deployment monitoring including RAG and agentic workflows. Architect, review, and approve CI/CD for ML and LLM systems: code, data, prompt, and model artifact versioning; build and release pipelines (Azure DevOps / GitHub Actions); automated unit, integration, and contract testing; and promotion/rollback (blue-green / canary) across dev, test, and production. Define and govern AI platform foundations on Azure: IaC (Bicep/Terraform), AML workspaces, AKS GPU node pools and scheduling, private networking (VNet integration / Private Link), identity (Managed Identities / PIM), secrets (Key Vault), and encryption and data residency controls. Review and approve production deployment patterns for model and LLM serving (AKS / KServe / AML online endpoints), including containerization, inference optimization (batching, quantization where applicable), API management, autoscaling, resiliency, and RAG runtime components (vector store, retriever, re-ranker, cache). Own observability and reliability for AI services: OpenTelemetry tracing, prompt and inference logs (with PII controls), latency/throughput/cost metrics, SLOs/SLIs, model performance monitoring, data and model drift detection, and LLM evaluations (quality, hallucination checks, toxicity and safety guardrails) with incident playbooks. Establish and enforce MLOps/LLMOps governance: dataset lineage, data quality validation (schema and tests), feature store and model registry standards, artifact provenance (SBOM/SLSA), vulnerability scanning, approval gates for model and prompt releases, and compliance-aligned documentation for model risk (intended use, limitations, evaluation results). Enable delivery squads including the primary delivery partner with "golden path" templates (AML pipelines, RAG blueprints, evaluation harnesses), reusable IaC modules, and coding standards; run deep technical design and architecture reviews and sign off production readiness (capacity, security, observability, DR) for all AI releases. Support the Run & Operate model by enabling issue triage and minor enhancement workflows (ticket intake fix controlled release), ensuring changes follow the same release governance and quality gates. Own the Operational Acceptance Gate: no production release without runbooks, monitoring dashboards, incident playbooks, access model, and DR test evidence. Scope clarity: you provide platform standards, review, and sign-off you do not replace the delivery partner's engineering, but you enforce the "golden path" and production readiness bar. What you bring: 8 10 years across DevOps, SRE, and/or ML Engineering with production systems on Azure. Hands-on experience with Azure ML, AKS, Azure DevOps or GitHub Actions, IaC, and containerization. Bachelor's in Computer Science, Engineering, or equivalent experience. Core skills required: Python, YAML, Docker, Helm, KQL; GitOps (Argo/Flux) awareness. Security in CI/CD: SAST/DAST, supply-chain security (Sigstore), secrets management (Key Vault). Performance testing (k6 / JMeter), contract testing, and E2E testing. Cost optimization and capacity planning for GPU and CPU workloads. Strong grasp of model serving, inference optimization, and observability tooling. Required certification: Microsoft Certified: DevOps Engineer Expert (AZ-400) Preferred certifications: Microsoft Certified: Azure Administrator (AZ-104) or Solutions Architect (AZ-305) CKA or CKAD (Kubernetes) Desired Candidate Profile 8 10 years across DevOps, SRE, and/or ML Engineering with production systems on Azure. Hands-on experience with Azure ML, AKS, Azure DevOps or GitHub Actions, IaC, and containerization. Bachelor's in Computer Science, Engineering, or equivalent experience. Company Industry RecruitmentPlacement FirmExecutive Search Department / Functional Area IT Software Keywords AI/ML/DevOps Engineer Get real-time job updates only on our App