MLOps Engineer

About the Role
We are looking for an MLOps Engineer to help us turn machine learning models into
reliable, scalable production systems. You will work at the intersection of ML,
cloud infrastructure, and DevOps, owning the full lifecycle of ML services — from
training pipelines to deployment and monitoring.
This role is hands-on and engineering-driven. You will work closely with ML
practitioners and software engineers to ensure models can be shipped, operated,
and improved safely in production.

What You’ll Do

• Build and operate end-to-end ML pipelines (data → training → deployment
  → monitoring).
• Deploy and maintain ML model serving systems using containerized and
  cloud-native approaches.
• Design and manage AWS infrastructure supporting ML workloads.
• Implement and improve CI/CD pipelines for ML (code, data, and models).
• Ensure scalability, reliability, and observability of ML services in
  production.
• Collaborate across teams to turn models into production-ready features.

• Continuously improve MLOps tooling, automation, and best practices.

  
  What We’re Looking For (Must-Have)

• Strong experience with Python in production environments.
• Solid software engineering fundamentals (clean code, testing, documentation).
• Hands-on experience with Docker and containerized deployments.
• Practical experience with CI/CD pipelines (e.g. GitHub Actions, GitLab CI).
• Strong Linux and Git workflow knowledge.
• Proven experience working with AWS, including:
  o Compute (EC2, containers, or serverless)
  o Storage and databases (S3, RDS, NoSQL)
  o Networking (VPCs, subnets, security groups)
  o IAM and least-privilege access
• Experience deploying and operating machine learning models in
  production.
• Familiarity with MLOps tools such as MLflow, DVC, Weights & Biases, or similar.

• Understanding of ML monitoring (model performance, drift, reliability).

  
  Nice to Have

• Experience with Kubernetes or managed orchestration platforms (EKS/ECS).
• Exposure to Infrastructure as Code (Terraform or CloudFormation).
• Knowledge of cloud and application security best practices (OWASP, IAM hardening, encryption).
• Experience integrating security checks into CI/CD pipelines.
• Ability to reason about system design and architectural trade-offs.
• Interest in mentoring, ownership, or technical leadership.
  What Success Looks Like
• You can take an ML model from notebook to production with confidence.
• You design systems that are observable, maintainable, and scalable.
• You proactively improve reliability, automation, and developer experience.

•  You balance ML performance, cost, security, and operational complexity.

  
  Certifications (Optional)

• AWS Certified Solutions Architect – Associate
• AWS Certified Machine Learning – Specialty
• CompTIA Security+