MLOps Engineer

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Job Description:

The MLOps Engineer is responsible for operationalizing, scaling, and maintaining enterprise AI/ML systems across cloud, hybrid, and on‑premise environments. The role focuses on enabling reliable delivery of LLM workloads, retrieval‑augmented generation (RAG), document intelligence, multimodal processing, and predictive/ML pipelines—supported by strong governance, observability, security, and automation.

Key Responsibilities:

• Build and automate end‑to‑end ML pipelines (data ingestion → feature engineering → training → evaluation → packaging → deployment).
• Establish model CI/CD workflows including versioning, automated testing, canary/blue‑green deployments, and rollback strategies.
• Operationalize LLM‑based and RAG systems (embedding workflows, vector indexing, latency optimization, grounding quality checks).
• Productionize document‑processing and multimodal workflows (OCR parsing, enrichment flows, batch/stream scaling).
• Implement observability (data quality, drift, safety indicators, inference latency, error conditions).
• Enforce Responsible AI controls (auditability, reproducibility, governance metadata, lineage, approval workflows).
• Maintain secure serving environments (container hardening, IAM, secrets, network isolation).
• Optimize GPU/CPU utilization, autoscaling, throughput, and cost efficiency. Create reusable templates, reference architectures, starter repos, and documentation.
  
  

Required Skills & Qualifications:

• Strong Python, CI/CD, Docker, Kubernetes.
• Experience operationalizing LLM, RAG, and predictive ML systems.
• Strong foundations in data engineering, schema governance, batch/stream pipelines.
• Security mindset (PII controls, secrets, network boundaries, auditability).
• Vertex AI (ML orchestration & CI/CD, training, tuning, deployment, model registry & monitoring).
• BigQuery / BigQuery ML (analytics & in‑warehouse ML).
• Cloud Composer Dataflow (batch/stream ETL orchestration).
• GKE or Cloud Run (secure, scalable model serving).
• Artifact Registry Cloud Build/Cloud Deploy (container & CI/CD).
  
  

Preferred Qualifications:

• Familiarity with agentic reasoning patterns and workflow chaining.
• Experience with LLM evaluation, grounding, bias/safety checks.
• Contributions to open-source ML/MLOps tooling.