Senior MLOps Engineer

• AI/ML

• Remote

• Contract

About the job:
Title – Senior MLOps Engineer
Start date: Immediate
Position Type: Contract/ FTE
Location: Remote across Canada/USA
  
  
About the Role: 
We are looking for a highly skilled Senior MLOps Engineer to lead the development, deployment, and operationalization of an in-house, on-premise code generation system. This role requires hands-on experience with GitLab Actions and a strong foundation in LLMOps to manage the lifecycle of large language models (LLMs) effectively. 
As a critical member of the team, you will design, implement, and optimize robust MLOps pipelines, ensuring the seamless operation of our code generator. Your contributions will be instrumental in integrating cutting-edge AI into our workflows while maintaining the highest standards of performance, security, and scalability. 
  
Key Responsibilities: 
GitLab CI/CD and Automation: 

• Design and implement CI/CD pipelines using GitLab Actions for model and code deployment in an on-prem environment. 
• Automate testing, deployment, and rollback processes for machine learning workflows. 

  
LLMOps Expertise: 

• Develop pipelines specifically tailored for managing large language models, including fine-tuning, version control, and automated deployments. 
• Implement monitoring systems to track model performance, latency, drift, and data quality. 

  
MLOps Pipeline Development: 

• Build scalable pipelines for model training, evaluation, and deployment, leveraging tools such as MLflow, Kubeflow, or Airflow. 
• Ensure reproducibility and traceability of experiments and models. 

  
Infrastructure and Security: 

• Architect and manage a secure, on-premise infrastructure optimized for high-performance compute environments (e.g., NVIDIA GPUs, TPUs). 
• Implement robust security practices for handling sensitive data and ensure compliance with industry standards. 

Collaboration and Integration: 

• Work closely with AI researchers, software engineers, and DevOps teams to integrate LLM-based code generation tools into existing systems. 
• Provide guidance on best practices for LLMOps and MLOps adoption across the organization. 

  
Optimization and Scalability: 

• Optimize LLM inference and training workflows for cost, speed, and efficiency. 
• Scale the system to support multiple users and high daily API calls within an on-premise setup. 

  
Documentation and Training: 

• Maintain detailed documentation for pipelines, infrastructure, and workflows. 
• Train team members and stakeholders on tools and practices for effective LLM and MLOps workflows. 

  
Key Requirements: 
  
Technical Skills: 
  
GitLab CI/CD: 

• Hands-on experience with GitLab Actions for CI/CD pipelines in machine learning projects. 
• Expertise in automating complex workflows with GitLab. 

  
LLMOps: 

• Proven experience managing large language models (LLMs) in production. 
• Familiarity with fine-tuning and deploying LLMs using tools like Hugging Face Transformers or OpenAI APIs. 
• Experience with continuous learning pipelines for LLMs. 

  
MLOps Fundamentals: 

• Strong skills in MLOps tools like MLflow, DVC, or Kubeflow. 
• Proficient in Python and machine learning libraries such as PyTorch or TensorFlow. 

  
Infrastructure: 

• Experience with containerization and orchestration (Docker, Kubernetes). 
• Knowledge of GPU-optimized workflows and distributed systems. 

  
Soft Skills: 

• Exceptional problem-solving and troubleshooting abilities. 
• Strong communication and collaboration skills to work with cross-functional teams. 
• Leadership qualities to mentor junior engineers and lead complex projects. 

  
Preferred Qualifications: 

• Experience with hybrid MLOps and DevOps workflows. 
• Understanding of secure on-prem deployments for AI/ML systems. 
• Knowledge of prompt engineering and evaluation techniques for LLMs.