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.