ML OPS Engineer Azure AI Foundry

MLOps Engineer Azure AI Foundry

Location: Hyderabad, Delhi, IN

Experience: 10+ years

Employment Type: Full-time

Role Summary

We are seeking a highly skilled MLOps Engineer with deep expertise in Azure AI Foundry, Azure ML, and cloudnative model operations. This role is responsible for building automated, secure, and scalable ML pipelines that support LLMs, retrievalaugmented generation (RAG), multiagent systems, and traditional ML models across the enterprise.

You will work closely with AI engineers, data engineers, architects, and product teams to operationalize models from experimentation through production, ensuring reliability, observability, governance, and continuous improvement.

Key Responsibilities

Azure AI Foundry & Model Lifecycle

• Operationalize AI workloads using Azure AI Foundry, including:

• Model catalog & versioning
• Prompt flow orchestration
• Agent orchestration

• Evaluation & safety tooling

• Build and maintain endtoend ML pipelines for training, validation, deployment, and monitoring.
• Manage model registries,lineage, and lifecycle governance.

CI/CD & Automation

• Implement automated ML workflows using:

• GitHub Actions

• Azure ML CLI / SDK

• Build reusable IaC modules for ML environments using Bicep or Terraform.
• Automate environment provisioning,feature store updates, and model deployment.

Data & Feature Engineering Operations

• Operationalize feature pipelines using:

• ADLS Gen2
• Azure Data Factory / Fabric Data Pipelines

• Delta Lake / Parquet

• Implement feature stores, data quality checks, and drift detection.

Model Deployment & Serving

• Deploy models to:

• Azure ML Managed Endpoints
• Azure Kubernetes Service (AKS)
• Azure Container Apps

• Azure Functions for lightweight inference

• Implement scalable, lowlatency inference patterns for LLMs and RAG workloads.

Monitoring, Observability & Governance

• Implement observability for:

• Model performance
• Data drift
• Prompt drift
• Latency & throughput

• Cost monitoring

• Integrate with Azure Monitor, Application Insights, Log Analytics, and custom dashboards.

• Enforce governance for:

• Responsible AI
• Model approval workflows
• Audit trails
• Security & compliance

Security & Zero Trust

• Implement secure ML architectures using:

• Private endpoints
• Managed identities
• Key Vault

• RBAC & workload identity federation

• Ensure compliance with enterprise and regulatory standards.

Required Skills & Experience

• 48+ years of experience in MLOps, ML engineering, or cloud engineering.
• Strong handson experience with Azure AI Foundry and Azure Machine Learning.

• Proficiency with:

• Python
• MLflow
• Azure ML SDK

• GitHub Actions / Azure DevOps

• Experience deploying and operating:

• LLMs
• RAG pipelines
• Multiagent systems

• Traditional ML models

• Strong understanding of:

• CI/CD
• IaC (Bicep/Terraform)
• Containerization (Docker)

• Kubernetes (AKS)

• Experience with data engineering tools (ADLS, ADF, Fabric, Databricks optional).

Preferred Qualifications

• Experience with:

• Vector databases (Azure AI Search, Redis, Pinecone)
• Prompt engineering & evaluation
• Dapr or eventdriven microservices

• Azure API Management for model APIs

• Azure certifications (AZ104, DP203, AI102, AZ305).
• Experience in P&C insurance, underwriting, or ACORDaligned data models (nice to have).

Soft Skills

• Strong communication and documentation skills.
• Ability to collaborate with architects, AI engineers, and product teams.
• Ownership mindset with a focus on automation, reliability, and continuous improvement.
• Comfortable working in a fastpaced, AInative product environment.