Über
Client is seeking an experienced Senior MLOps Engineer to join client’s Data Science Enablement (MLOps) team as a contractor. Candidates will be the primary MLOps expert for a product area, working as part of a cross-functional agile team and owning the successful launches and ongoing operations of high-profile products in production. Candidate’s focus will be on driving new feature delivery, maintaining operational excellence, and collaborating on enhancements to client’s shared ML platform. Candidates will work closely with cross-functional collaborators—including ML researchers, product managers, and platform engineers—to deliver scalable, reliable, and low-latency ML solutions. This contract position has potential to transition into a full-time role in the future based on performance and business needs. Key Responsibilities
Design, implement, and deploy new features and enhancements to ML products, collaborating with Product and ML Research teams to refine requirements. Technical ownership of existing and new production ML products in your area, ensuring alignment of technical investments with business goals (in collaboration with a product owner) and engineering best practices. End-to-end technical stewardship of ML products, ensuring ongoing reliability and performance. Contribute to the evolution of the shared ML platform alongside other MLOps engineers to drive best practices and shared tooling across all products. Maintain and improve automated CI/CD pipelines, testing frameworks, and monitoring/logging, ensuring high operational standards. Conduct comprehensive code reviews to enforce coding standards, improve code quality, and share knowledge. Identify and implement opportunities for process, tooling, and system improvements, proactively addressing technical debt and scaling challenges. Oversee pre-release testing, coordinate releases, and ensure smooth enablement of new features. Provide technical guidance and support to other engineers and data scientists to solve complex technical challenges. Mentor and coach other engineers, supporting their professional growth. Foster a culture of collaboration, continuous improvement, and knowledge sharing. Proactively identify and resolve blockers, navigate processes, and independently seek out information and connect with relevant teams to drive solutions in the face of ambiguity. Operate with a strong sense of urgency, consistently prioritizing and executing tasks to meet timelines and deliver results. Experience and Skills - Required
5 plus years as an ML Engineer, MLOps Engineer, or similar, with hands-on production experience Proven expertise with ML model deployment, API design, and integration into production environments Strong Python programming and relevant ML/data libraries Experience with containerization, orchestration, and AWS cloud services Building and operating CI/CD pipelines (Jenkins preferred) Experience designing and configuring low-latency databases to serve real-time features, such as DynamoDB Monitoring, troubleshooting, and optimizing production ML systems Pre-release testing and release management Demonstrated ability to work independently, navigate ambiguity, and deliver results Excellent communication skills and ability to collaborate in cross-functional teams Experience and Skills - Preferred
Experience with MLFlow, model versioning, and storage Exposure to GenAI/NLP, AutoML, model explainability Familiarity with Databricks or similar platforms Experience supporting high-volume, real-time data products Automated testing and validation frameworks Why Join Client?
Impact: Play a key role as the technical owner of high-profile ML products delivering meaningful business impact to merchants and advancing key pillars of the company’s strategy. Candidate’s work will directly influence the reliability, scalability, and evolution of critical production systems. Autonomy: Take end-to-end technical ownership of candidate’s product area, with the freedom and responsibility to drive technical solutions, shape best practices, and deliver results in a fast-paced, supportive environment. Collaboration: Join a cross-functional, high-performing team where candidate’s expertise is valued and candidate’s contributions make a real difference. Note: Team: Data Science Enablement (MLOps) Rate card: $85.92 - $130.00 hourly. Any candidate submitted with a bill rate over $130 will be rejected. Work schedule and time zone: Eastern Time 8.00 am to 5.00 pm Overtime Eligible(Y/N): N Workstoppage Eligible (Y/N): HM looking into this Remote (Y/N): Y Potential to convert and/or extend: Yes.
Sprachkenntnisse
- English
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