How you will do it
ML Platform Engineering & MLOps (Azure-Focused)
Build and manage end-to-end ML/LLM pipelines on Azure ML using Azure DevOps for CI/CD, testing, and release automation.
Operationalize LLMs and generative AI solutions (e.g., GPT, LLaMA, Claude) with a focus on automation, security, and scalability.
Develop and manage infrastructure as code using Terraform, including provisioning compute clusters (e.g., Azure Kubernetes Service, Azure Machine Learning compute), storage, and networking.
Implement robust model lifecycle management (versioning, monitoring, drift detection) with Azure-native MLOps components.
Infrastructure & Cloud Architecture
Design highly available and performant serving environments for LLM inference using Azure Kubernetes Service (AKS) and Azure Functions or App Services.
Build and manage RAG pipelines using vector databases (e.g., Azure Cognitive Search, Redis, FAISS) and orchestrate with tools like LangChain or Semantic Kernel.
Ensure security, logging, role-based access control (RBAC), and audit trails are implemented consistently across environments.
Automation & CI/CD Pipelines
Build reusable Azure DevOps pipelines for deploying ML assets (data pre-processing, model training, evaluation, and inference services).
Use Terraform to automate provisioning of Azure resources, ensuring consistent and compliant environments for data science and engineering teams.
Integrate automated testing, linting, monitoring, and rollback mechanisms into the ML deployment pipeline.
Collaboration & Enablement
Work closely with Data Scientists, Cloud Engineers, and Product Teams to deliver production-ready AI features.
Contribute to solution architecture for real-time and batch AI use cases, including conversational AI, enterprise search, and summarization tools powered by LLMs.
Provide technical guidance on cost optimization, scalability patterns, and high-availability ML deployments.
Qualifications & Skills
Required Experience
Bachelor’s or Master’s in Computer Science, Engineering, or a related field.
5+ years of experience in ML engineering, MLOps, or platform engineering roles.
Strong experience deploying machine learning models on Azure using Azure ML and Azure DevOps.
Proven experience managing infrastructure as code with Terraform in production environments.
Technical Proficiency
Proficiency in Python (PyTorch, Transformers, LangChain) and Terraform, with scripting experience in Bash or PowerShell.
Experience with Docker and Kubernetes, especially within Azure (AKS).
Familiarity with CI/CD principles, model registry, and ML artifact management using Azure ML and Azure DevOps Pipelines.
Working knowledge of vector databases, caching strategies, and scalable inference architectures.
Soft Skills & Mindset
Systems thinker who can design, implement, and improve robust, automated ML systems.
Excellent communication and documentation skills—capable of bridging platform and data science teams.
Strong problem-solving mindset with a focus on delivery, reliability, and business impact.
Preferred Qualifications
Experience with LLMOps, prompt orchestration frameworks (LangChain, Semantic Kernel), and open-weight model deployment.
Exposure to smart buildings, IoT, or edge-AI deployments.
Understanding of governance, privacy, and compliance concerns in enterprise GenAI use cases.
Certification in Azure (e.g., Azure Solutions Architect, Azure AI Engineer, Terraform Associate) is a plus.