DevOps / MLOps Engineer

    Build and maintain CI/CD pipelines for frontend, backend services, data pipelines, ML models, and GenAI/LLM apps across dev/test/prod environments.

·      Implement automated build, test, security scanning, packaging, and deployment workflows (including approvals and release gates where required).

·      Create and maintain deployment templates and “golden paths” (service scaffolds, pipeline templates, IaC modules) to accelerate product teams.

·      Own runtime operations for deployed systems: environment management, configuration, secrets, rollout/rollback strategies, and release coordination.

·      Enable ML/LLM operationalization: model packaging, deployment patterns (batch + real-time), model/version promotion, and basic evaluation checks in pipelines.

·      Enable LLMOps workflows: prompt/version management practices, RAG app deployment patterns, monitoring for latency/cost, and safe logging/redaction practices.

·      Implement observability for services and pipelines (logs/metrics/traces), alerting, dashboards, and on-call/incident response runbooks.

·      Partner with security and architecture to implement IAM, least privilege, network controls, encryption, vulnerability management, and auditability.

·      Manage containerization and runtime platforms (Docker/Kubernetes or equivalent) and ensure consistency across environments.

·      Support performance and cost optimization for AI workloads (compute sizing, autoscaling, caching, token/cost tracking where applicable).

·      Troubleshoot release and production issues, conduct root cause analysis, and drive preventive fixes through automation and standards.