AI / ML Engineer

• Seniority: 6+ years of experience in an MLOps, DevOps, or Data Engineering role with a heavy focus on productionizing machine learning models.
• Python Mastery: Exceptional Python programming skills with a deep understanding of asynchronous programming, performance profiling, and backend frameworks like FastAPI.
• Production ML Scale: Proven track record of deploying and monitoring ML models at scale. You know how to handle high-concurrency traffic, model drifting, and resource optimization.
• Cloud & Data Proficiency: Strong expertise in the GCP ecosystem and writing complex, optimized SQL queries for large datasets.
• Tooling Agility: Comfortable working across a diverse ecosystem of package managers and frameworks, with a keen interest in adopting high-performance tools (like uv).
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience).

Job responsibilities

• Infrastructure & Automation: Design, provision, and maintain scalable ML infrastructure on GCP using Terraform.
• Scalable Deployment: Architect and deploy high-throughput, low-latency ML inference endpoints capable of handling heavy production traffic.
• Pipeline Orchestration: Build, monitor, and optimize robust data and ML pipelines using GCP Cloud Composer / Apache Airflow.
• Distributed Computing: Implement and scale distributed training and data processing workloads using Ray.
• MLOps Evolution: Transition our current logging and metadata tracking from native GCP tools toward advanced open-source stacks (e.g., MLflow) as our infrastructure matures.
  • Collaboration & Standards: Partner with Data Science teams to standardize package management (utilizing uv, Poetry, etc.) and establish best practices for code quality, containerization (Docker), and model reproducibility.