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.
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