We are seeking a Lead MLOps Engineer with over 7 years of hands-on experience in cloud infrastructure, software engineering, and AI/ML lifecycle management. In this role, you will architect, build, and maintain our enterprise MLOps platform on Microsoft Azure. You will serve as the technical bridge between AI/ML research teams and production systems, ensuring scalable model deployment, robust CI/CD automation, and reliable cloud messaging pipelines.

Key Responsibilities

As a Lead MLOps Engineer, your responsibilities will include:

- AI Model Lifecycle Management: Lead the deployment, scaling, and governance of foundation and custom models using Azure AI Foundry (Azure OpenAI Service / Model Catalog) and Azure Machine Learning.
- ML DevOps & Platform Engineering: Design, build, and maintain enterprise-grade CI/CD pipelines (via Azure DevOps or GitHub Actions) tailored for AI engineering workflows, model versioning, and continuous monitoring.
- Code Modularization & Productionization: Drive best practices for converting exploratory data science code (e.g., Jupyter Notebooks) into clean, production-ready Python modules, microservices, and serverless architectures (e.g., Azure Functions).
- API Architecture & Governance: Configure, secure, and manage AI model endpoints using Azure API Management (APIM), enforcing rate-limiting, authentication, logging, and key routing across internal and external teams.




- Asynchronous Integration: Architect event-driven messaging patterns using Azure Service Bus to decouple long-running ML/LLM workloads, background inference tasks, and ingestion streams.
- Technical Leadership: Mentor mid-level and junior MLOps engineers, establish code quality and testing standards, and collaborate closely with Lead Data Scientists and Solution Architects.

Requirements & Qualifications

To succeed in this role, you should possess:

- Experience: A minimum of 7 years of core DevOps, Data, or Platform Engineering experience, with at least 3 years specifically focused on MLOps and production AI systems.
- Azure Expertise: Deep expertise across the Microsoft Azure ecosystem, including Azure Functions, Azure AI Foundry, Azure ML, Managed Identities, and Azure Networking/Security principles.
- API Management: Solid hands-on experience configuring Azure APIM (policies, products, developer portals, rate limiting, and backend routing for OpenAI/LLM endpoints).
- Messaging & Integration: Solid understanding of event-driven architectures using Azure Service Bus, Event Grid, or similar queueing services.
- DevOps Infrastructure: Expertise with Infrastructure as Code (e.g., Terraform, Bicep, ARM templates) and containerization (e.g., Docker, Azure Container Apps/Instances).

📌 Lead MLOps Engineer (Delhi)
🏢 Microforge
📍 Delhi