Senior Machine Learning Operations Developer- Inference, AI​/ML Platform

Operational Efficiency:Drive the operational excellence of our AI/ML Platform by implementing andoptimizingMLOpspractices

Deployment Automation:Design and implement automated deployment pipelines for machine learning models, ensuring seamless transitions from development to production

Scalable Infrastructure:Collaborate with cross-functional teams to design, implement, andmaintainscalable infrastructure for model training, inference, and data processing

Monitoring and Logging:Develop andmaintainrobust monitoring and logging systems to track model performance, system health, and overall platform efficiency

Collaboration with Data Developers:Work closely with data developers to ensure efficient data pipelines for model training and validation

Version Control and Model Governance:Implement version control systems for machine learning models and contribute to model governance practices

Governance and Trust:Contribute to the implementation of robust model governance practices, version control systems, and adherence to compliance standards. Uphold data privacy and ethical considerations, fostering trust in our AI/ML solutions

Security and Compliance:Enforce security best practices and compliance standards in all aspects ofMLOps, ensuring data privacy and platform security

Continuous Improvement:Identifyopportunities for process automation, optimization, and implement strategies to enhance the overallMLOpslifecycle

Troubleshooting and Incident Response:Play a key role inidentifyingand resolving operational issues, contributing to incident response and system recovery

Educational Background:BS or MS in Computer Science, or related field

MLOps

Experience:

5+ years of hands-on experience in Dev Ops andMLOps, with a focus on deploying and managing machine learning models in production environments

Infrastructure as Code (IaC):Proficiencyin implementing Infrastructure as Code practices using tools such as Terraform or Ansible

Containerization:Strongexpertisein containerization technologies (Docker, Kubernetes) for orchestrating and scaling machine learning workloads

CI/CD:Demonstratedexperience in setting up and managing Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning projects

Scripting and Automation:Strong scripting skills in Python, Bash, or similar languages for automating operational processes

Monitoring Tools:Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) for tracking system and model performance

Security Awareness:Understanding ofsecurity best practices inMLOps, including data encryption, access controls, and compliance standards

Collaboration

Skills:

Excellent collaboration and communication skills, working effectively with cross-functional teams including data developers, software developers, and researchers

Problem-solving

Skills:

Proven ability to troubleshoot and resolve complex operational issuesin a timely manner

Cloud

Experience:

Experience with cloud platforms, especially AWS or Azure, for deploying and managing machine learning infrastructure

Database Knowledge:Familiarity with databases and data storage solutions commonly used inMLOps, such as SQL, No

SQL, or data lakes

Machine Learning Frameworks:Exposure to popular machine learning frameworks (Tensor Flow,PyTorch) and their integration intoMLOpsprocesses

Collaboration Tools:Previousexperience with collaboration tools like Git for version control and Jira for project management

Agile…