ML Platform Engineer

AI/ML Platform Engineer

Job Title: ML Platform Engineer – AI & Data Platforms

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.