Your 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, workplace 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.
Operational mindset with solid attention to reliability, security, incident response, and cost-performance trade-offs.

Your Profile
Minimum 5+ 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.
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.

(ref:hirist.tech)

📌 Thyssenkrupp Materials Services Ml/agent Ops Engineer Mumbai (India)
🏢 JSW
📍 India