Senior MLOps Engineer, Cohere Health

• This role requires a blend of technical expertise and leadership skills to elevate our ML operations to the next level
• Master's or Ph.D. (preferred) in Computer Science, Engineering, or a related field
• 5+ years of experience in MLOps, ML Engineering, or related roles
• Proven track record of deploying and managing large-scale machine learning models, especially LLMs
• Deep expertise in programming languages such as Python
• Extensive experience with ML frameworks like TensorFlow or PyTorch
• Experience with cloud platforms (AWS preferred, GCP, or Azure) and containerization technologies (Docker, Kubernetes)
• Strong understanding of CI/CD pipelines, automation tools, and infrastructure as code
• Familiarity with big data technologies (Spark, Hadoop) and distributed computing
• Experience with monitoring and logging tools (e.g. MLFlow, Tensorboard, ELK Stack)
• Exceptional problem-solving skills and the ability to navigate complex technical challenges

Desirable

• You will be leading the deployment, management, and optimization of advanced ML models, including LLMs
• You will collaborate with cross-functional teams to architect scalable solutions, improve our core technologies, and drive the strategic direction of our ML infrastructure
• Strategic Leadership
• Define and implement the overall MLOps strategy to support the deployment and scaling of ML models
• Lead the architectural design of ML infrastructure, ensuring scalability, reliability, and efficiency
• Collaborate with leadership to align ML initiatives with business objectives
• LLM Deployment and Management
• Oversee the deployment, scaling, and optimization of LLMs
• Develop strategies to reduce latency and manage costs effectively
• Stay updated on the latest advancements in LLMs and integrate relevant technologies
• Core Technology Enhancement
• Lead the development and improvement of core technology components and system architecture
• Work with engineering teams to ensure seamless integration of ML solutions into products and services
• Evaluate and implement new tools and technologies to enhance the ML infrastructure
• Automation and Efficiency
• Architect and build automated ML pipelines, including data preprocessing, model training, and deployment
• Implement robust CI/CD pipelines for ML models to facilitate continuous integration and delivery
• Optimize workflows to increase efficiency and reduce time-to-market for ML solutions
• Monitoring and Optimization
• Establish monitoring systems to track model performance, data drift, and system health
• Proactively identify and resolve issues to maintain high availability and reliability of ML services
• Perform regular audits and optimizations to enhance system performance
• Mentorship and Collaboration
• Mentor and guide junior ML engineers in MLOps best practices
• Foster a culture of innovation, collaboration, and continuous improvement within the team
• Engage with cross-functional teams to drive cohesive and integrated solutions
• Security and Compliance
• Ensure all ML systems comply with security standards and regulatory requirements
• Implement data governance policies and practices to protect sensitive information