MLOps Engineer

Role Overview As an MLOps Engineer, you will be the vital link between our Data Scientists and our Production Engineering teams. You will design, build, and maintain the infrastructure and pipelines that enable the rapid, reliable, and repeatable deployment, monitoring, and governance of machine learning models at scale. Your expertise will transform proof-of-concept models into high-impact production systems.

Key Responsibilities :

• ML Platform & Infrastructure:
• Design, implement, and manage scalable, secure, and cost-effective cloud infrastructure (GCP or Azure) for ML workloads (training, serving, batch inference).
• Containerize ML applications using Docker and orchestrate them with Kubernetes or managed services (EKS, GKE, AKS).
• CI/CD & Automation for ML:
• Build and maintain robust CI/CD pipelines specifically tailored for ML (MLOps pipelines) using tools like GitHub Actions, GitLab CI, Jenkins, or CircleCI.
• Automate the entire ML lifecycle: data validation, model training, evaluation, packaging, deployment, and rollback.
• Implement model versioning, data versioning (e.g., DVC), and experiment tracking (e.g., MLflow, Weights & Biases, Neptune) to ensure full reproducibility.
• Model Deployment & Serving: ● Monitoring, Observability & Governance:

Key Qualifications:

• Experience: 5-7 years of professional experience, with at least 3 years focused on MLOps, ML Engineering, or a related field in a production environment.
• Cloud & Infrastructure: Strong hands-on experience with at least one major cloud provider (AWS, GCP, Azure). Cloud certification is a plus.
• Containerization & Orchestration: Expertise in Docker and Kubernetes.
• CI/CD & Automation: Proven experience building and maintaining CI/CD pipelines. Understanding of MLOps principles and tools (MLflow, Kubeflow, etc.).
• Programming: Proficiency in Python and familiarity with common ML frameworks (scikit-learn, TensorFlow, PyTorch, XGBoost). Strong shell scripting skills.
• Data & Versioning: Experience with data pipeline tools (Apache Airflow, Prefect) and version control for data and models (DVC, Git LFS).
• Monitoring: Experience with observability tools for both infrastructure and model performance.
• Soft Skills: Excellent problem-solving, communication, and collaboration skills. Ability to translate complex technical concepts for diverse audiences. Proactive and self-motivated.

Educations:

• A Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or a closely related technical/quantitative field is the standard foundation we look for.
• We value practical engineering skill over academic pedigree. Equivalent professional experience—proven through a strong track record of building and maintaining production ML systems—is often more relevant than a specific degree.
• Relevant certifications can be a plus (e.g., AWS Certified Machine Learning – Specialty, Google Professional ML Engineer, Kubernetes CKA/CKAD)