Responsibilities-

- Design and implement CI/CD pipelines for models, prompts, agents, and supporting infrastructure across development, test, and production environments.
- Build and maintain deployment automation, versioning, rollback mechanisms, environment promotion workflows, and runtime safeguards for AI workloads.
- Set up and operate observability for AI applications and agents, including tracing, monitoring, alerting, token consumption analysis, latency tracking, and incident diagnostics.
- Implement evaluation pipelines and acceptance gates for quality, groundedness, task adherence, safety, and agent-specific behavior.
- Drive prompt lifecycle management, RAG optimization, semantic retrieval tuning, and integration of vector-based or search-based knowledge components where needed.
- Collaborate with security, engineering, and data teams to embed identity, secrets management, compliance controls, and cost optimization into the operating model.
- Contribute to platform automation, runbooks, on-call readiness, and post-incident improvement for production AI services.

Experiences-

- Minimum 5–7+ years of experience in DevOps, platform engineering, MLOps, or a closely related role.




- Experience operating production cloud workloads with CI/CD, monitoring, and infrastructure automation.
- Experience with production AI, ML, or agentic workloads is strongly preferred.
- Experience working with high-availability, regulated, or enterprise-scale environments is an advantage.

Skills-

- Strong experience with Azure, GitHub or Azure DevOps, Docker, Kubernetes, Terraform or Bicep, and infrastructure-as-code patterns.
- Hands-on experience with MLOps, LLMOps, or AgentOps practices for deployment, monitoring, retraining or reevaluation, and controlled release management.
- Solid understanding of observability concepts, including logs, metrics, traces, runtime telemetry, and production diagnostics for AI systems.
- Practical Python skills for automation, tooling, evaluation orchestration, and operational support.
- Familiarity with retrieval-augmented systems, prompt engineering, tool-calling flows, and agent behavior debugging.
- Operational mindset with strong attention to reliability, security, incident response, and cost-performance trade-offs.

📌 DevOps Engineer (Mumbai)
🏢 thyssenkrupp Materials IT Services India
📍 Mumbai