MLOps Engineer (W2 only)

This opening is a contract opportunity with potential for full-time conversion. As such, our client is seeking candidates immediately hire-able who will not require sponsorship in the future

Our client has an immediate need for a
MLOps Engineer
to support the development, deployment, and maintenance of large-scale ML pipelines. This role will collaborate closely with cross-functional teams to optimize workflows, ensure system reliability, and contribute to internal MLOps frameworks.

**Technical Skills:

Must-Have (5+ years):**

• 5+ years of experience in software engineering, data engineering, or MLOps
• Expert-level proficiency in Python, including Pandas, PySpark, and PyArrow
• Expert-level proficiency in the Hadoop ecosystem, distributed computing, and performance tuning
• Experience with CI/CD tools and best practices in ML environments
• Experience with monitoring tools and techniques for ML pipeline health and performance
• Strong collaboration skills in cross-functional teams

Nice-to-Have:

• Experience contributing to internal MLOps frameworks or platforms
• Familiarity with SLURM clusters or other distributed job schedulers
• Exposure to Kafka, Spark Streaming, or other real-time data processing tools
• Knowledge of model lifecycle management, including versioning, deployment, and drift detection

Education / Certifications:

• Bachelor’s degree in a technical field preferred
• SAFe certification is a plus

Key Responsibilities

• Optimize and maintain large-scale feature engineering jobs using PySpark, Pandas, and PyArrow on Hadoop infrastructure
• Refactor and modularize ML codebases to improve reusability, maintainability, and performance
• Collaborate with platform teams to manage compute capacity, resource allocation, and system updates
• Integrate with Model Serving Framework for testing, deployment, and rollback of ML workflows
• Monitor and troubleshoot production ML pipelines, ensuring high reliability, low latency, and cost efficiency
• Contribute to internal Model Serving Framework by proposing improvements and documenting best practices.