Senior MLOps Engineer

Role Overview

We are seeking an experienced and strategic Senior MLOps Engineer to evolve and strengthen our globally deployed recommender system. The platform already delivers measurable value across multiple countries, and we are now entering a new stage of architectural and operational maturity.

This is a senior, hands-on role combining deep technical expertise, architectural ownership, and mentorship. You will lead the advancement of our ML systems with a strong focus on MLOps excellence, production reliability, and scalable cloud-based infrastructure.

Responsibilities

• Architect & Evolve: Lead the architectural evolution of our live recommender system to enhance scalability, performance, and reliability.

• Champion MLOps: Define and implement MLOps best practices, including CI/CD pipelines in GitLab, experiment tracking in MLflow, and production monitoring.

• Productionalize Models: Collaborate with data scientists to transition advanced ML models from research to production using AWS SageMaker.

• Strengthen Observability: Establish robust monitoring, logging, and alerting mechanisms for ML systems.

• Mentor & Lead: Act as a technical advisor and mentor to data scientists, data engineers, and MLOps engineers.

• Hands-On Development: Contribute directly to the Python codebase, delivering clean, maintainable, and well-tested solutions for ML infrastructure and deployment.

• Collaborate Cross-Functionally: Work closely with product managers, business stakeholders, and engineering teams to translate business requirements into scalable ML solutions.

• Drive Innovation: Identify and advocate for modern tools and architectural improvements to continuously enhance ML capabilities.

Must Have

• Bachelor’s degree in Computer Science, Engineering, or related technical field (or equivalent practical experience).

• 5+ years of professional experience in Machine Learning Engineering with a proven track record of deploying and maintaining production ML systems.

• Strong experience designing, documenting, and communicating complex ML and data architectures.

• Proven mentoring and technical leadership experience.

• Expert-level Python programming skills with deep knowledge of the data science ecosystem.

• Hands-on experience with AWS SageMaker (model training, deployment, optimization).

• Experience with at least one major deep learning framework (TensorFlow or PyTorch).

• Strong practical experience across the MLOps lifecycle, including:

  • MLflow (experiment tracking, model registry)

  • GitLab CI/CD (automation & deployment)

• Experience designing scalable ML systems in a cloud environment (AWS preferred).

• Ability to collaborate with business stakeholders and translate requirements into technical solutions.

Nice to Have

• Master’s or PhD in Computer Science, AI, Machine Learning, or related field.

• Experience with model monitoring & observability tools (e.g., Prometheus, Grafana, Evidently AI).

• Strong communication skills and ability to explain complex technical decisions to non-technical audiences.