MLOps Engineer
Summary
Build and deploy machine learning models and services, focusing on MLOps practices, API development, and cloud infrastructure using Python, AWS, and tools like MLflow and Databricks.
Designation: Consultant
Job Location: India (Remote)
Experience: 2 - 4 years
- 2–4 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)
- 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