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Lithy Tree Technology Services in Thane is hiring a Sr. MLOps & AgentOps Engineer to industrialise ML models and AI agents in production on Azure and Foundry. You will own end-to-end CI/CD, deployments, and runtime controls for secure, cost-effective operations.
The role requires a strong background in DevOps, MLOps, and enterprise-scale environments, with hybrid work and client-facing engagement.
Hi,
We are having opening for a Sr. MLOps & AgentOps Engineer (Azure & Microsoft Foundry) - Thane
Job Summary: Lithy Tree Technology Services is hiring a Senior MLOps & AgentOps Engineer to industrialise the full lifecycle of machine learning models and AI agents in production for an enterprise client. This senior engineering role builds the operational backbone across the clients Azure and Microsoft Foundry estate: CI/CD, automated releases, evaluation pipelines, observability, and runtime controls. The engineer will ensure AI workloads are deployed reliably, measured rigorously, and run securely and cost-effectively. Working with security, engineering, and data teams, they will turn AI solutions into dependable production services. This is an immediate opening for an engineer who thrives on reliability and automation.
Own end-to-end architecture, integration, security, governance, and scalable AI/agent platform design for enterprise environments. Enterprise Data & AI Architecture on Azure, with strong expertise across Microsoft Fabric, Purview, Foundry, and Copilot Studio.
Bachelors degree in Computer Science, Information Technology, Engineering, or a related field.
Fulltime
Hybrid weekly 3 days from office
1
Thane- client location
Male/Female
As per the industry 25L to 32L
2 Technical Round
Design and implement CI/CD pipelines for models, prompts, agents, and supporting infrastructure across development, test, and production environments.
Build and maintain deployment automation for AI workloads, including versioning and rollback mechanisms, environment promotion workflows, and runtime safeguards.
Set up and operate observability for AI applications and agents: 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 behaviour.
Drive prompt lifecycle management, RAG optimisation, and semantic retrieval tuning, and integrate vector- or search-based knowledge components where needed.
Partner with security, engineering, and data teams to build identity, secrets management, compliance controls, and cost optimisation into the operating model.
Contribute to platform automation, runbooks, and on-call readiness, and lead post-incident reviews that improve production AI services.
Immediate