Job Description

Job Title: Senior MLOps Engineer

Location: Remote (Canada/ US)

Duration: Long Term

Mandatory Skills: LLM, LLMops, Github actions

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.

What We Offer:

• A chance to work on cutting-edge AI/ML technologies in an innovative environment.
• Competitive compensation and comprehensive benefits.
• The opportunity to shape and lead a mission-critical AI initiative.

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