AI/ML/DevOps Engineer
Skills
About This Role
Overview
- 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)
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