MLOps Engineer

Role Overview

We are seeking a seasoned MLOps Engineer with 6+ years of hands-on experience to join our growing AI/ML team. You will be responsible for designing, building, and maintaining scalable and reliable machine learning infrastructure and pipelines.

What You Will Do

Design, implement, and maintain robust CI/CD pipelines for ML models and data workflows. Automate and monitor the training, validation, deployment, and performance tracking of ML models.

Why It Might Be a Fit

This role is ideal for someone who thrives at the intersection of data science, software engineering, and DevOps. You will collaborate with data scientists, ML engineers, and DevOps teams to ensure production-grade ML systems.

Requirements

• Bachelors or Masters degree in Computer Science, Engineering, or a related field
• 6+ years of experience in software development, DevOps, and/or machine learning infrastructure
• Strong experience with cloud platforms such as AWS, Azure, or GCP
• Proficiency in containerization (Docker) and orchestration (Kubernetes)
• Solid knowledge of CI/CD tools like Jenkins, GitLab CI, CircleCI, etc.
• Experience with ML lifecycle tools (MLflow, Kubeflow, SageMaker, Vertex AI, etc.)
• Strong coding skills in Python and experience with ML libraries (scikit-learn, TensorFlow, PyTorch)
• Familiarity with monitoring tools like Prometheus, Grafana, or ELK stack
• Experience with data pipeline tools (Airflow, Luigi, Prefect)