MLOps / Cloud Deployment Engineer

• Own CI/CD pipelines for ML models, RAG applications, and agentic AI systems - from experiment to production
• Deploy and operate AI workloads on cloud-native ML/AI platforms - AWS Bedrock/SageMaker, Azure AI Foundry / Azure Machine Learning, or equivalent
• Build and maintain observability, tracing, and monitoring for LLM and agentic systems - latency, cost, hallucination rates, tool-call success, drift detection
• Implement model governance and guardrails - approval gates, kill-switches, escalation paths, audit trails
• Manage infrastructure-as-code (Terraform, Bicep, or equivalent) for reproducible AI/ML environments
• Design cost and performance optimization strategies - token usage tracking, caching, model routing, autoscaling, warehouse/cluster right-sizing
• Own security posture - RBAC, secret management (Key Vault / Secrets Manager), prompt-injection risk mitigation, auditability for regulated pharma
• Partner with data engineers, AI engineers, and Finance business stakeholders to move systems from prototype to reliable production
• Implement evaluation frameworks for AI systems in production - regression testing, adversarial testing, accuracy tracking, hallucination monitoring

• 5+ years in cloud/DevOps/MLOps engineering on AWS, Azure, or GCP
• Production deployment of ML or GenAI systems - CI/CD, containerization (Docker/Kubernetes), infrastructure-as-code (Terraform)
• MLOps tooling - MLflow, SageMaker Pipelines, Azure ML Pipelines, or equivalent
• LLM/GenAI operational experience - observability tools (LangSmith, Weights & Biases, or equivalent), cost monitoring, latency optimization, prompt/model versioning
• Cloud-native AI platforms - hands-on with at least one of: AWS Bedrock, SageMaker, Azure AI Foundry, Azure OpenAI, Vertex AI
• Python, Bash, and infrastructure scripting - strong
• Security and governance in regulated environments - RBAC, secrets, audit, compliance

• Pharma, life sciences, or regulated financial services domain
• Experience operating agentic AI systems in production - multi-agent orchestration, tool-calling, human-in-the-loop workflows
• LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel operational experience
• Kubernetes-native ML platforms (Kubeflow, Ray)
• Snowflake or Databricks operational experience (compute governance, cost management)
• Certifications: AWS/Azure ML Engineer, Kubernetes CKA/CKAD, Terraform Associate

• Data Scientists or research engineers - this is a production platform role
• Application developers with light DevOps exposure - need real MLOps/cloud engineering depth
• Pure infra engineers with no AI/ML operational experience - need to understand what makes LLM systems different (evals, hallucinations, prompt versioning, RAG grounding)

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