AI/ML Platform Engineer - ML Platform Engineering & MLOps (Azure-Focused) at Johnson Controls, Pune, Maharashtra, India

Johnson Controls is seeking an experienced AI/ML Platform Engineer to join their team in Pune, Maharashtra, India. This role focuses on building and managing advanced AI and Data Platforms, with a strong emphasis on MLOps within the Azure ecosystem.

ML Platform Engineering & MLOps (Azure-Focused)

• Develop and maintain end-to-end Machine Learning and Large Language Model (LLM) pipelines on Azure ML, leveraging Azure DevOps for continuous integration, continuous delivery, testing, and release automation.
• Operationalize LLMs and generative AI solutions, including models like GPT, LLaMA, and Claude, ensuring automation, robust security, and scalability.
• Utilize Infrastructure as Code (IaC) with Terraform for provisioning and managing compute clusters such as Azure Kubernetes Service (AKS) and Azure Machine Learning compute, along with storage and networking resources.
• Implement comprehensive model lifecycle management, encompassing versioning, real-time monitoring, and drift detection, utilizing Azure-native MLOps components.

Infrastructure & Cloud Architecture

• Design highly available and performant serving environments for LLM inference, utilizing Azure Kubernetes Service (AKS) and Azure Functions or App Services.
• Build and manage Retrieval Augmented Generation (RAG) pipelines using vector databases like Azure Cognitive Search, Redis, or FAISS, and orchestrate workflows with tools such as LangChain or Semantic Kernel.
• Ensure consistent implementation of security measures, logging, Role-Based Access Control (RBAC), and audit trails across all environments.

Automation & CI/CD Pipelines

• Construct reusable Azure DevOps pipelines for the deployment of ML assets, including data pre-processing, model training, evaluation, and inference services.
• Employ Terraform to automate the provisioning of Azure resources, guaranteeing consistent and compliant environments for data science and engineering teams.
• Integrate automated testing, linting, monitoring, and rollback mechanisms into the ML deployment pipeline to enhance reliability and efficiency.

Collaboration & Enablement

• Collaborate closely with Data Scientists, Cloud Engineers, and Product Teams to deliver production-ready AI features.
• Contribute to the solution architecture for both real-time and batch AI use cases, such as conversational AI, enterprise search, and summarization tools powered by LLMs.
• Provide technical guidance on cost optimization, scalability patterns, and best practices for high-availability ML deployments.

Qualifications & Skills

Required Experience

• A Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Minimum of 5 years of experience in ML engineering, MLOps, or platform engineering roles.
• Demonstrated 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 (including libraries like PyTorch, Transformers, LangChain) and Terraform, with scripting skills in Bash or PowerShell.
• Experience with containerization using Docker and orchestration with Kubernetes, particularly within Azure (AKS).
• Familiarity with CI/CD principles, model registry management, 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

• A systems thinking approach to design, implement, and improve robust, automated ML systems.
• Excellent communication and documentation skills, fostering effective collaboration between platform and data science teams.
• A strong problem-solving mindset focused on delivery, reliability, and achieving business impact.

Preferred Qualifications

• Experience with LLMOps, prompt orchestration frameworks (LangChain, Semantic Kernel), and the deployment of open-weight models.
• Exposure to smart buildings, IoT, or edge-AI deployments.
• Understanding of governance, privacy, and compliance considerations for enterprise Generative AI use cases.
• Azure certifications (e.g., Azure Solutions Architect, Azure AI Engineer) or Terraform Associate certification are advantageous.