Job description

Are you passionate about building enterprise-scale Generative AI solutions that solve real-world operational challenges? We are looking for a Senior AI / ML Engineer to join our Data, AI & Energy Practice. In this role, you will design, build and deploy production-ready AI applications that integrate with complex enterprise data platforms, asset telemetry and mission-critical workflows. This is more than a prompt engineering or prototype-focused position. You will work within an empowered Agile delivery team to architect scalable Retrieval-Augmented Generation (RAG) applications, agentic workflows and AI-powered services on the Microsoft Azure ecosystem. You will partner with data engineers, software developers, cloud architects and business stakeholders to turn advanced AI capabilities into reliable, measurable business outcomes. The ideal candidate is a hands-on engineer with strong Python, Azure AI, cloud-native deployment and data integration experience. Experience in energy, industrial operations, IoT telemetry or similarly complex business domains is a strong advantage. Req# 1098563370 Responsibilities: • Design and develop end-to-end Generative AI applications using Azure OpenAI, Azure AI Foundry and Microsoft Copilot Studio • Architect and implement RAG solutions, including document ingestion, chunking, embedding generation, vector indexing, retrieval, reranking and response orchestration • Build agentic AI workflows that integrate enterprise knowledge, APIs, tools and business processes • Develop secure, high-performance backend APIs and microservices using Python • Integrate AI services with existing enterprise applications and backend platforms, including .NET/C# services where needed • Containerize applications with Docker and deploy workloads to Azure Kubernetes Service (AKS), Azure App Services and Azure Functions • Build and maintain automated CI/CD pipelines using GitHub Actions • Work with data engineering teams to connect AI applications with data platforms such as Azure Data Factory, Azure Synapse and Databricks • Implement vector storage, semantic search, caching and retrieval capabilities using Azure AI Search and related Azure services • Participate in architecture reviews, code reviews and technical design discussions • Help establish engineering standards for AI application quality, security, observability, testing and operational support • Collaborate closely with product owners, data engineers, architects and energy-domain stakeholders to translate business problems into practical AI solutions Requirements: • 4+ years of professional experience in software engineering, machine learning engineering or a related technical discipline • Strong production-level development experience in Python • Hands-on experience building applications with Azure OpenAI • Strong understanding of LLMs, prompt engineering, RAG architectures, embeddings and vector search • Experience with Azure AI Search, including vector indexing and retrieval patterns • Experience designing and building REST APIs, microservices or distributed backend services • Practical experience with Docker and cloud application deployment • Experience deploying solutions using Azure services such as AKS, Azure App Services and/or Azure Functions • Experience working with data platforms or pipelines built on Databricks, Azure Data Factory or Azure Synapse • Strong technical ownership and the ability to solve ambiguous architectural and engineering challenges independently • Effective communication skills and the ability to collaborate with cross-functional technical and business teams Nice to have: • Experience with Microsoft Copilot Studio and agentic AI development • Experience using Azure AI Foundry • Familiarity with C#/.NET and enterprise backend integration patterns • Background in Upstream Oil & Gas, wells, subsurface, production operations, industrial systems or IoT/asset telemetry • Experience rapidly learning complex business domains and translating domain challenges into technical solutions • Microsoft Azure certifications, including: AI-102: Azure AI Engineer Associate, AZ-204: Developing Solutions for Microsoft Azure, DP-100: Designing and Implementing a Data Science Solution on Azure

Requirements

Must-haves

Are you passionate about building enterprise-scale Generative AI solutions that solve real-world operational challenges?
Req# 1098563370
Design and develop end-to-end Generative AI applications using Azure OpenAI, Azure AI Foundry and Microsoft Copilot Studio
Architect and implement RAG solutions, including document ingestion, chunking, embedding generation, vector indexing, retrieval, reranking and response orchestration
Build agentic AI workflows that integrate enterprise knowledge, APIs, tools and business processes
Develop secure, high-performance backend APIs and microservices using Python
Integrate AI services with existing enterprise applications and backend platforms, including .NET/C# services where needed
Containerize applications with Docker and deploy workloads to Azure Kubernetes Service (AKS), Azure App Services and Azure Functions
Build and maintain automated CI/CD pipelines using GitHub Actions
Work with data engineering teams to connect AI applications with data platforms such as Azure Data Factory, Azure Synapse and Databricks
Implement vector storage, semantic search, caching and retrieval capabilities using Azure AI Search and related Azure services
Participate in architecture reviews, code reviews and technical design discussions
Help establish engineering standards for AI application quality, security, observability, testing and operational support
Collaborate closely with product owners, data engineers, architects and energy-domain stakeholders to translate business problems into practical AI solutions
4+ years of professional experience in software engineering, machine learning engineering or a related technical discipline
Strong production-level development experience in Python
Hands-on experience building applications with Azure OpenAI
Strong understanding of LLMs, prompt engineering, RAG architectures, embeddings and vector search

Nice-to-haves

Experience with Microsoft Copilot Studio and agentic AI development
Experience using Azure AI Foundry
Familiarity with C#/.NET and enterprise backend integration patterns
Background in Upstream Oil & Gas, wells, subsurface, production operations, industrial systems or IoT/asset telemetry
Experience rapidly learning complex business domains and translating domain challenges into technical solutions
Microsoft Azure certifications, including: AI-102: Azure AI Engineer Associate, AZ-204: Developing Solutions for Microsoft Azure, DP-100: Designing and Implementing a Data Science Solution on Azure

Application prep

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Keywords to mirror

aimlagileazurepythondockerkubernetesci/cdmachine learningrest

Platform: epam
Effort: medium