Senior Machine Learning Operations Developer: AI​/ML Platform

Operational Efficiency: Drive the operational excellence of our AI/ML Platform by implementing and optimizing MLOps practices

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, and maintain scalable infrastructure for model training, inference, and data processing

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

Collaboration with Data Engineers: Work closely with data engineers 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 of MLOps, ensuring data privacy and platform security

Continuous Improvement: Identify opportunities for process automation, optimization, and implement strategies to enhance the overall MLOps lifecycle

Troubleshooting and Incident Response: Play a key role in identifying and 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 and MLOps, with a focus on deploying and managing machine learning models in production environments

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

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

CI/CD: Demonstrated experience 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 processe

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

Security Awareness: Understanding of security best practices in MLOps, including data encryption, access controls, and compliance standards

Collaboration

Skills:

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

Problem-solving

Skills:

Proven ability to troubleshoot and resolve complex operational issues in 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 in MLOps, such as SQL, No

SQL, or data lakes

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

Collaboration Tools: Previous experience with collaboration tools like Git for version…