Description :

Role Summary :

We are hiring a DevOps & MLOps Platform Engineer to enable reliable, secure, and repeatable delivery of AI and data products across the organization.

This role will own the end-to-end engineering enablement layer for product releases spanning CI/CD for backend, frontend, data pipelines, ML models, and GenAI/LLM applications.

The engineer will work closely with product engineering, data science, and architecture teams to standardize deployment patterns, improve developer productivity, strengthen observability, and ensure production readiness across environments (cloud and/or on-prem).

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.

Key Skills :

- 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 :

1. Packaging and deploying model artifacts

2. handling versioning and rollback

3. enabling monitoring hooks (model/service health, latency, drift signals as defined by DS teams)

4. supporting LLM app deployment (RAG services, vector DB connections, prompt/config versioning)

Skills Required :

- 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) 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.

Qualifications :

- Bachelors degree in Computer Science, Engineering, or related field (or equivalent hands-on experience).

- 3 to 6 years of experience in DevOps / Platform Engineering / SRE / MLOps / LLMOps or adjacent roles with demonstrated ownership of CI/CD and production deployments.

- Demonstrated ability to support end-to-end releases for full-stack applications (frontend + backend) and automate deployments across environments.