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.