MLOps Engineer

EXL

8d • Remote

About The Position

We are seeking an MLOps Engineer with 2-4 years of hands-on experience in software/data/ML engineering in production environments. The ideal candidate will have very strong Python skills, experience building and deploying APIs/services, and working knowledge of AWS. You will need to understand the end-to-end ML lifecycle, have working knowledge of SQL and familiarity with PySpark, and possess CI/CD and version-control fundamentals. Prior hands-on MLOps tooling experience and experience supporting GenAI or LLM workloads operationally are highly desirable.

Requirements

• 2-4 years of hands-on experience in software/data/ML engineering in production environments
• Very strong Python skills (clean, production-quality code)
• Sharp problem-solving skills and aptitude to pick up MLOps practices quickly
• Experience building and deploying APIs/services (FastAPI, Flask, or similar)
• Working knowledge of AWS (EC2, S3, Lambda)
• Understanding of the end-to-end ML lifecycle (training vs inference pipelines, deployment, and monitoring)
• Working knowledge of SQL and familiarity with PySpark
• 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

Nice To Haves

• Ability to read, refactor, and convert code for other environments
• Exposure to Databricks or comparable platforms
• Candidates who can join immediately will be prioritized

Responsibilities

• Build and deploy APIs/services (FastAPI, Flask, or similar)
• Host and serve applications using AWS (EC2, S3, Lambda)
• Understand the end-to-end ML lifecycle, including training vs inference pipelines, deployment, and monitoring
• Work with SQL and PySpark, and refactor/convert code for different environments
• Implement CI/CD and version-control fundamentals (Git, testing, rollback)
• Utilize Docker for containerization
• Support GenAI or LLM workloads operationally, including model serving, inference pipelines, and cost/performance tuning

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level