AI Engineer – Azure AI Foundry Specialist

About the role

SD Worx is seeking an AI Engineer to lead the adoption of Azure AI Foundry for intelligent automation across its payroll and product services. The role focuses on designing, building, and delivering AI‑driven solutions that eliminate repetitive tasks and improve efficiency for internal teams.

Key responsibilities

• Design and implement automation solutions using Azure AI Foundry, including model orchestration, vector search, secure prompt pipelines, and evaluation tooling.
• Develop and maintain Python‑based AI services that integrate with Foundry workflows, AI hubs, models, and datasets.
• Create reusable AI components, best‑practice patterns, and guidelines for prompt design, model governance, and safe deployment.
• Build end‑to‑end intelligent automation for high‑frequency, high‑effort processes such as document understanding, NLP workflows, agentic automation, and retrieval‑augmented generation.
• Integrate AI solutions with enterprise systems via secure APIs, micro‑services, and Python automation scripts.
• Collaborate with payroll and product SMEs to identify automation opportunities and translate business challenges into technical designs.
• Ensure solutions follow engineering best practices, including version control, testing, observability, monitoring, and responsible AI principles.
• Continuously evaluate emerging Azure Foundry features and incorporate them into the automation roadmap.

Required profile

• Proven experience designing and delivering AI‑driven automation solutions in an enterprise environment.
• Strong ability to work with cross‑functional teams and translate business needs into technical specifications.
• Commitment to responsible AI, model evaluation, and governance.

Required skills

• Python programming
• Azure AI Foundry
• Azure cloud services
• Machine learning model orchestration
• Vector search and embeddings
• Prompt engineering and secure prompt pipelines
• Natural language processing (NLP)
• Retrieval‑augmented generation (RAG)
• API development and micro‑services
• Data ingestion and transformation pipelines