MLOps Engineer

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.