AI/ML/DevOps Engineer

Client of Faze 3 Consulting

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)