Role overview
**\*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.