ML / Applied AI Engineer

ML / Applied AI Engineer (MLOps / Applied ML Platform Engineering Targeted SME Support)

Required Qualifications

• 6+ years of experience in Applied ML Engineering, MLOps, or ML Platform Engineering
• Strong understanding of ML model lifecycle management (data training deployment monitoring)
• Experience designing training and feature pipeline architectures
• Proven ability to build production-grade systems for:
• model packaging and deployment
• versioning and artifact management
• evaluation and monitoring
• Experience with LLMs in production (inference patterns, evaluation, cost/latency tradeoffs, iteration workflows)
• Strong software engineering skills (Python preferred) with clean coding, testing, and documentation practices
• Comfortable working in an embedded model with PR workflows, shared repos, and collaborative development

Preferred / Nice-to-Have

• Experience with orchestration and ML workflow tools (Airflow, Prefect, Dagster, Kubeflow, etc.)
• Familiarity with model registries and experiment tracking (MLflow or similar)
• Experience with containerized deployment and cloud-based ML environments
• Strong perspective on LLM evaluation frameworks, governance controls, and operational safety patterns
• Background building internal developer tooling or reusable ML platform components