ML Ops Engineer

Job description

**Job Responsibilities:** - Design, build, and operate end-to-end ML systems - data ingestion, training pipelines, deployment, monitoring, and retraining - Develop and deploy batch and real-time inference services (REST APIs) for production ML workloads - Package, convert, and migrate ML code across environments - from platforms such as Databricks to containerized AWS-based deployments (including serverless functions) - Build CI/CD pipelines and workflow orchestration for ML systems, including versioning, promotion, environment isolation, and rollbacks - Monitor models and services in production, implement drift detection, and drive retraining and re-deployment - Partner with data scientists and engineers to build models and support broader initiatives such as data collection projects **Skills:** **Must have:** - 24 years of hands-on experience in software/data/ML engineering in production environments - Very strong Python - clean, production-quality code (not just notebooks); sharp problem-solving and the aptitude to pick up MLOps practices quickly - Experience building and deploying APIs/services (FastAPI, Flask, or similar) and working knowledge of AWS (EC2, S3, Lambda) for hosting and serving - Understanding of the end-to-end ML lifecycle - training vs inference pipelines, deployment, and monitoring - Working knowledge of SQL and familiarity with PySpark; exposure to Databricks or comparable platforms, with the ability to read, refactor, and convert code for other environments - CI/CD and version-control fundamentals (Git, testing, rollback); familiarity with Docker; strong ownership and comfort with a broad, evolving scope and global stakeholders - Prior hands-on MLOps tooling experience (MLflow, model registries, drift detection, ML observability) - Experience supporting GenAI or LLM workloads operationally (model serving, inference pipelines, cost/performance tuning) **Eligibility:** - Master's or Bachelor's degree in Computer Science, Engineering, Math, Statistics, or a related field - 2–4 years of relevant hands-on experience; candidates who can join immediately will be prioritized