DevOps / MLOps Engineer

Key Responsibilities

• 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.

Must Have Skill Sets

• Strong fundamentals in DevOps practices: CI/CD, release management, environment promotion, and automation-first mindset.
• Hands-on experience with source control and pipeline tooling (e.g., GitHub/GitLab/Azure DevOps/Jenkins or equivalents).
• Solid experience with containers (Docker) and basic orchestration concepts (Kubernetes or similar).
• Working knowledge of Infrastructure-as-Code concepts and tools (e.g., Terraform/CloudFormation/Ansible or equivalents).
• Strong understanding of Linux, networking basics, and application deployment patterns.
• Experience implementing observability: centralized logging, metrics, dashboards, tracing, alerting, and incident response hygiene.
• Security basics: secrets management, artifact signing or provenance awareness, dependency scanning, vulnerability remediation workflows, and access control patterns.
• Ability to support ML/GenAI delivery from an operations standpoint:
• • packaging and deploying model artifacts
  • handling versioning and rollback
  • enabling monitoring hooks (model/service health, latency, drift signals as defined by DS teams)
  • supporting LLM app deployment (RAG services, vector DB connections, prompt/config versioning)

Good to Have Skill Sets

• Experience with ML tooling concepts (model registry, MLflow or equivalent, feature store awareness).
• Exposure to LLMOps tools/patterns (RAG deployments, vector databases, LLM gateways, prompt/version tooling).
• Familiarity with cloud services (AWS/Azure/GCP) or on-prem equivalents, including IAM and networking.
• Experience with service mesh, API gateways, caching, or event streaming platforms.
• Prior experience supporting data orchestration tools (Airflow/Prefect or similar) and data-quality checks in pipelines.

Education

• Bachelor's degree in Computer Science, Engineering, or related field (or equivalent hands-on experience).

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