Sr. MLOps & AgentOps Engineer- Azure - Thane

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.
Key Focus Area: 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.
Position / DesignationSr. MLOps & AgentOps EngineerQualificationBachelors degree in Computer Science, Information Technology, Engineering, or a related field.Years of Experience

• Minimum 57+ years in DevOps, platform engineering, MLOps, or a closely related role.
• Experience running production AI, ML, or agentic workloads is strongly preferred.
• Exposure to high-availability, regulated, or enterprise-scale environments is an advantage.

Permanent / Contract (If contract, period?)FulltimeHybrid / RemoteHybrid weekly 3 days from office Number of post1LocationThane- client location GenderMale/Female Annual CTC / SalaryAs per the industry 25L to 32LSelection Process2 Technical RoundJob Role & ResponsibilityDesign 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. Skills Technical Skills:

• Cloud & IaC: Azure, Terraform or Bicep, infrastructure-as-code patterns.
• CI/CD & containers: GitHub Actions or Azure DevOps Pipelines, Docker, Kubernetes (AKS).
• AI platform: Microsoft Foundry (deployments, evaluations, tracing), Azure AI Search, vector stores.
• Observability: Azure Monitor, Application Insights, Log Analytics, OpenTelemetry.
• Programming: Python for automation, tooling, evaluation orchestration, and operational support.

Desired Attributes:

• Strong analytical and problem-solving abilities with meticulous attention to detail.
• Effective communicator able to interface with engineering, data, security, and operations stakeholders.
• Self-motivated professional comfortable delivering in a hybrid model, balancing on-site client engagement with remote work.
• Collaborative team player committed to continuous improvement.

Certifications:

• Microsoft Certified: DevOps Engineer Expert (AZ-400) or Azure Administrator Associate (AZ-104) preferred.
• Microsoft Certified: Azure AI Engineer Associate (AI-102) or successor, or Fabric Data Engineer Associate (DP-700), a plus.

Qualifications:

• DevOps and platform engineering: Experience operating production cloud workloads with CI/CD, monitoring, and infrastructure automation.
• MLOps / LLMOps / AgentOps: Hands-on practice in deployment, monitoring, retraining or re- evaluation, and controlled release management for AI systems.
• Observability: Strong grasp of logs, metrics, traces, runtime telemetry, and production diagnostics for AI workloads.
• Generative AI engineering: Familiarity with retrieval-augmented systems, prompt engineering, tool-calling flows, and agent behaviour debugging.
• Operational excellence: A reliability-first mindset with strong attention to security, incident response, and cost-performance trade-offs.

Joining DateImmediate