Job Description

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.