ML/Agent Ops Engineer
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Reach the decision-maker — $5About the role
**Company presentation**
https://www.tkaccelis.com/
**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, 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. * Operational mindset with strong attention to reliability, security, incident response, and cost\-performance trade\-offs.
**Your profile**
* 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**
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* 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. * Strong 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.
**Contact details**
tkMits\-IN\-Recruitment@thyssenkrupp\-materials.com
Job Reference: 0 RS APA00072
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