DevOps Engineer

Responsibilities:
Design, build, and continuously improve CI/CD pipelines for both traditional backend services and ML workloads.
Debug and resolve issues across ML pipelines from data ingestion to model serving, working closely with the data science team.
Develop and maintain Python-based backend services and tooling that support our ML infrastructure.
Propose and implement MLOps automation solutions: model versioning, experiment tracking, automated retraining, and monitoring.
Manage cloud infrastructure (AWS/GCP/Azure), container orchestration (Docker, Kubernetes), and IaC tools (Terraform, Pulumi).
Monitor production systems, set up alerting, and ensure high availability of ML-powered features.
Collaborate with data scientists and backend engineers in an agile environment to ship reliable, scalable systems.
Requirements:
2-3+ years of experience in DevOps, backend engineering, or MLOps roles.
Strong Python skills: you can write production-grade backend code, not just scripts.
Solid understanding of ML algorithms and workflows (training, evaluation, and deployment) enough to debug pipeline issues and have informed conversations with data scientists.
Hands-on experience designing and maintaining CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, or similar).
Experience with containerization (Docker) and orchestration (Kubernetes).
Familiarity with ML tooling: MLflow, Kubeflow, Airflow, DVC, or equivalent.
A proactive, ownership-driven mindset: you identify bottlenecks and propose solutions before being asked.
Comfort with agile workflows and fast iteration cycles: You thrive in environments where priorities shift and quality still matters.
Qualifications: Bachelor's / Master's Degree in CS / ECE / EE / AI / ML Data Science.

Experience
2-4 yrs