What are the responsibilities and job description for the ML Ops Engineer position at Factspan?
Role: ML Ops Engineer
Dallas, TX (Remote)
Factspan Overview:
Factspan is a pure play data and analytics services organization. We partner with fortune 500 enterprises to build an analytics center of excellence, generating insights and solutions from raw data to solve business challenges, make strategic recommendations and implement new processes that help them succeed. With offices in USA, Canada, UK, Europe & India; we use a global delivery model to service our customers. Our customers include industry leaders from Retail, Media & Entertainment, Hospitality, and Logistic & Supply Chain.
Job Description:
We are seeking a highly skilled Senior MLOps Engineer with 8 years of experience to join our team. The ideal candidate will have extensive expertise in productionizing and operationalizing several large models, scalability, distributed data processing, team leadership experience including partnering with data science teams and mentoring ML engineers. Must be strong in Kubernetes.
Responsibilities
- Workflow Design & Implementation: Oversee the implementation of workflows for AI programming and team communication, ensuring optimal collaboration and efficiency.
- Model Deployment: Manage and optimize model deployment processes, including the use of Kubernetes for containerized model deployment and orchestration.
- Model Registry Management: Maintain and manage a model registry to track versions and ensure smooth transitions from development to production.
- CI/CD Implementation: Develop and implement Continuous Integration/Continuous Deployment (CI/CD) pipelines for model training, testing, and deployment, ensuring high code quality through rigorous model code reviews.
- Model Monitoring & Optimization: Design and implement model inference pipelines and monitoring frameworks to support thousands of models across various pods, optimizing execution times and resource usage.
- Team Leadership & Training: Manage, mentor, and train junior engineers, fostering their growth and learning while overseeing a large team
- Collaboration with Data Science Teams: Train and collaborate with data science team members on best practices in tools such as Kubeflow, Jenkins, Docker, and Kubernetes to ensure smooth model productionization.
- Reusable Frameworks Development: Draft designs and apply reusable frameworks for drift detection, live inference, and API integration.
- Cost Optimization Initiatives: Propose and implement strategies to reduce operational costs, including optimizing models for resource efficiency, resulting in significant annual savings.
- Documentation & Standards Development: Produce MLE standards documents to assist data science teams in deploying their models effectively and consistently.
Required Qualifications & Experience:
- 5 years of experience in MLOps, model deployment, and productionizing machine learning models.
- Proficient in Kubernetes, model monitoring, and CI/CD practices. Experience working in the Azure environment.
- Strong understanding of model registry concepts and best practices.
- Experience with programming languages and ML frameworks (e.g., TensorFlow, PyTorch).
- Proven track record of optimizing ML workflows and processes.
- Excellent communication and leadership skills, with experience in mentoring and training team members.
- Ability to work in a fast-paced, collaborative environment.
Why Should You Apply?
Experience a Hyper-growth Journey: Join a startup poised for exponential growth, offering abundant opportunities for learning and innovation.
People: Collaborate with a talented, warm, and cohesive team alongside accomplished leaders.
Dynamic Culture: Participate in a stimulating work atmosphere, where daily innovation and problem-solving create a vibrant environment.
www.factspan.com