Senior MLOps Engineer

What you'll do

• Design, operate, and improve the ML platform, including Databricks workflows for training, MLflow and Unity Catalog for model management, CI/CD pipelines using GitHub Actions and Databricks Asset Bundles, and integration with the runtime inference layer
• Support data scientists in developing and maintaining end-to-end training pipelines, covering data processing, feature transformations, ML model training on GPUs, and distributed training setups to ensure efficient and scalable workflows
• Ensure reliable and safe model inference by validating models before promotion, checking metadata compatibility, monitoring latency, and supporting model evaluation flows
• Maintain strong ML observability, including online and offline monitoring, data and feature parity checks, and automated validation of model performance in production
• Develop internal tools and APIs that enable DS teams and account managers to experiment, validate, and promote models efficiently while reducing operational friction

Requirements

• 3+ years of experience in MLOps, ML Platform Engineering, or Backend roles supporting production ML systems
• Strong Python and software engineering skills, with experience building and maintaining APIs (FastAPI, Flask, or similar)
• Hands-on experience with DNNs using PyTorch or other modern deep learning frameworks, including GPU training and optimizing hardware utilization
• Solid understanding of distributed computing and data processing using Apache Spark or similar frameworks
• Experience with Databricks (Jobs, MLflow, Delta, UC) or an equivalent cloud-based ML platform
• Strong understanding of the ML lifecycle: data ingestion, experimentation, model packaging, deployment, and monitoring
• Familiarity with real-time model inference systems and ensuring compatibility with downstream services
• Experience with data validation libraries (Pydantic or similar) used within ML and API workflows
• Good understanding of Docker containers, Kubernetes, and basic networking concepts
• Experience creating and maintaining CI/CD pipelines (GitHub Actions or similar)
• Experience with orchestration tools (Databricks Jobs, Dagster, Airflow or similar)
• Demonstrated ability to use modern AI tools (Claude, ChatGPT, Gemini) to improve development speed and quality
• Proactive mindset with a strong sense of ownership, consistently pushing work forward and anticipating next steps without needing close direction
• Strong English communication skills

Tech stack

Benefits