DevOps Engineer

Job duties and accountabilities

Roles & Responsibilities

Infrastructure & Cloud Operations

• Design, provision, and maintain cloud infrastructure (GCP/AWS) for AI, geospatial, and data-intensive workloads across the Tech & AI portfolio
• Manage compute environments for ML training, inference, and large-scale satellite data processing (Dask/Coiled, Vertex AI, GPU/TPU workloads)
• Optimise cloud spend through right-sizing, autoscaling, spot/preemptible usage, and storage lifecycle policies
• Maintain data pipeline infrastructure for geospatial ETL (NetCDF/Zarr/GeoTIFF), vector/graph databases, and RAG/agentic systems

CI/CD & Release Engineering

• Build and own CI/CD pipelines (GitHub Actions / GitLab CI) for backend services, ML models, dashboards, and data pipelines
• Establish containerisation standards using Docker and orchestration via Kubernetes / Cloud Run

Observability, Reliability & Security

• Set up monitoring, logging, and alerting stacks (Cloud Logging, Sentry) across applications, APIs, and ML services
• Implement security controls: IAM, secrets management (Vault / Secret Manager), vulnerability scanning, network policies

Data Platform Support

• Support API infrastructure for platforms like CRAVIS — gateway, rate limiting, authentication, versioning
• Partner with AI, geospatial, and product teams to unblock environment, deployment, and scaling issues

Selection Criteria

Qualification

Educational

• B.E./B.Tech in Computer Science, IT, or equivalent from a reputed engineering institute. Relevant certifications (GCP Professional DevOps Engineer, AWS DevOps, CKA) preferred

Must Have (Hands-on Experience)

• 4+ years operating production cloud infrastructure on GCP or AWS
• Strong hands-on with Docker, Kubernetes
• Solid scripting in Python and Bash; comfort with Linux internals and networking
• Experience building and maintaining CI/CD pipelines for both application and data/ML workloads, working knowledge of observability tooling
• Experience managing PostgreSQL/PostGIS or similar databases in production

Preferred

• Experience supporting ML/AI workloads, GPU provisioning, model serving (Vertex AI Endpoints, AWS Boto), MLOps tools (MLflow, Weights & Biases)
• Familiarity with geospatial data stacks (PostGIS, GeoServer, TiTiler, COG/Zarr workflows)
• Exposure to LLM/agentic infrastructure: vector databases, embedding pipelines, observability for agents
• Experience with government cloud or DPI-aligned deployments
• Prior work in climate tech, public-data systems, or mission-driven organisations

Compensation

Competitive compensation – commensurate with the experience and matching the best of the standards adopted by the industry or other similar organisations for similar roles.

Application Process

CEEW is an equal-opportunity employer, and the selection process does not discriminate on the basis of age, gender, caste, ethnicity, religion, or sexuality. Female candidates are encouraged to apply.

Applications will be reviewed on a rolling basis. Interested applicants are advised to apply at the earliest possible time.

Only shortlisted candidates will be notified by us. We appreciate your interest.