Azure AI Engineer

Overview

The AI Engineer designs, builds, deploys, and maintains AI models and systems that enable scalable

business solutions. Leveraging cloud platforms such as Microsoft Azure, the role develops

production-ready AI pipelines and integrates intelligent capabilities into enterprise applications.

Working closely with Data Engineers, Software Engineers, and Product Teams, the AI Engineer ensures

efficient data flow, reliable model deployment, and continuous performance optimization. The role is

responsible for improving model accuracy, scalability, and reliability in real-world environments.

The AI Engineer also upholds best practices in data quality, security, governance, and Responsible AI,

ensuring solutions are ethical, compliant, and aligned with organizational and regulatory standards.

Job Description:

Design, build, and deploy AI models and end-to-end AI pipelines for production environments

Integrate AI capabilities into applications and services using APIs and cloud-native architectures

Collaborate with Data Engineers, Software Engineers, and Product Teams to ensure seamless

data flow and system integration

Monitor, evaluate, and optimize model performance, accuracy, and scalability in real-world use

Develop and manage Prompt Flows, orchestration pipelines, and agent-based AI workflows

Implement Retrieval-Augmented Generation (RAG) solutions, including embedding, indexing,

and context management

Ensure adherence to Responsible AI practices, including model safety, governance, and

compliance standards

Establish observability, logging, and performance monitoring for AI systems

Apply secure-by-design principles, including identity management, access control, and data

protection

Translate business requirements into AI solutions, defining guardrails, KPIs, and success criteria.

Requirements:

Related Work Experience – 3–6+ years in AI/ML, software engineering, or cloud-based AI

solution development

a. Hands-on experience building and deploying AI/LLM-powered applications in

production

b. Experience with Azure or similar cloud platforms (AWS/GCP)

c. Proven work on prompt engineering, orchestration, or RAG-based solutions

d. Experience collaborating in cross-functional product or engineering teams

Knowledge – knowledgeable in the following:

a. AI/LLM engineering: prompt design, orchestration (Prompt Flow), agent-based systems,

and RAG implementation

b. c. Software engineering: Python, APIs (REST/JSON), microservices, and CI/CD practices

Azure AI ecosystem: AI Foundry, model deployment, inference APIs, and cost