Azure AI Engineer

Description

Job Summary

As an Azure AI Engineer with Generative AI specialization, you will design, develop, and deploy artificial intelligence, machine learning, generative AI and agentic AI solutions within the Azure ecosystem, including Azure AI Service. You will collaborate with data scientists, software engineers, product managers, and other stakeholders to build robust AI applications that address real-world business challenges, leveraging the power of Large Language Models (LLMs) and other generative technologies.

Key Responsibilities

Design AI Solutions

Designing dynamic UIs for chatbots, assistants and visualizations.

Applied experience in translating business requirements into full stack Azure AI Solutions combination of Azure AI Services (previously Azure Cognitive Services and Applied AI Services), Azure AI Search, Azure OpenAI, Azure Cosmos DB, Copilot Studio as well as RAG, NLU, CLU, Vision AI for Agentic Architecture.

Design multi-step prompt workflows, leverage RAG patterns to integrate LLMs with enterprise data, design intelligent agents that are task-based and/or role-based by using responsible AI principles.

Build AI Solutions

Experience building dynamic UIs using React.js for chat assistants, visualizations and dashboards.

Project experience leveraging PromptFlow to orchestrate prompts, tools, and grounding data using Azure Foundry (formerly Azure AI Studio).

Extend Microsoft Copilots using Copilot Studio or Graph Plugins.

Build custom AI Agents through Copilot Studio and/or Azure AI Foundry through plugin architecture, API function calling, logic apps, planner and memory constructs, storage of prompts, conversation history (Azure Cosmos DB), Azure Key Vault, Container Apps, Blob Storage, Cosmos DB.

Orchestrate LLM-based Agents by using LangChain and/or Semantic Kernel SDK (Python).

Experience using Git, Azure DevOps for building reliable CI/CD pipelines and containerized deployment of workflows.

Monitoring and Observability

Define and Implement metrics to evaluate prompt effectiveness and model behavior (adjust temperature settings).

Include logging, tracing and observability patterns for LLMs to address data drifts and hallucinations using tools such as Azure App Insights, Cosmos DB Logs.

Proactively troubleshoot performance bottlenecks and implement corrective measures.

Data Governance

Work with data engineers to build and optimize required data pipelines for data chunking, enrichment, and reinforcement learning.

Ensure data quality, integrity, and security throughout the AI development lifecycle.

Collaboration and Communication

Collaborate cross-functionally (software engineering, product, design, etc.) to align AI solutions with business goals.

Document and communicate technical designs, roadmaps, and updates to both technical and non-technical stakeholders.

Research and Innovation

Stay current on emerging AI/ML, Generative AI and Agentic AI trends, frameworks, and tools, recommending advancements that can enhance our AI initiatives.

Champion a culture of continuous learning and experimentation across the organization.

Qualifications

Education:

Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field (or equivalent experience).

Experience:

4+ years of professional experience in AI/ML engineering, with production-grade model integration.

2.5+ years of hands-on programming experience using Azure OpenAI or similar LLM APIs.

Expert-level proficiency in Python, Semantic Kernel or LangChain, Git-based workflows, and cloud-native design.

Experience building microservices and APIs on Azure (Functions, Containers, etc.).

Knowledge of LLM evaluation and prompt tuning techniques.

Knowledge of responsible AI usage and guidelines in design and development of AI solutions

Experience deploying open-source language models in real-world workflows.

Technical Skills:

Proficiency in Python (preferred), Semantic Kernel or LangChain or PromptFlow for orchestration of LLM-based Agentic AI workflows

Required hands-on experience with key Azure AI services (Azure Machine Learning, Azure OpenAI Service, Azure Cognitive Services, Azure Cosmos DB, Azure Databricks, Azure Data Factory).

Familiarity with Azure Copilot Studio, Azure AI Studio, Azure AI Foundry.

Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines (Git, Azure DevOps).

Strong understanding of machine learning algorithms, LLM model fine-tuning using prompts and RAG, LLMOps.

Soft Skills:

Excellent problem-solving and analytical abilities.

Strong communication and collaboration skills.

Ability to work in a fast-paced, agile environment, managing multiple priorities effectively.

Preferred Qualifications

Relevant Azure certifications (Azure AI Engineer Associate, Azure Solutions Architect, etc.).

Demonstrated experience with large-scale model deployment (LLMs, image generation, multimodal models).

Proficiency in React is a plus in building dynamics UIs and dashboards.

Familiarity with GraphRAG architecture, Caching architecture, Reinforcement Learning architecture for LLM-based solutions.

Experience with Agile/Scrum methodologies.

Proficiency with data visualization and business intelligence tools (Power BI, Tableau).