AI Software Engineer (Azure AI, Java/Angular Full-Stack)

Principal Responsibilities

• Develop and implement AI models and algorithms using Microsoft Azure technologies to optimize business processes and improve efficiency.
• Uses process design technology methodologies, programming languages and tools, and solutions design techniques to develop full-stack applications to meet business specifications.
• Performs analysis, design, development, and testing of applications to solve business requirements, actively leveraging Azure AI to provision resources, manage foundation model endpoints, and orchestrate LLM workflows.
• Builds and tunes production-grade RAG pipelines (including document ingestion, semantic chunking, embedding generation, vector indexing, and hybrid retrieval optimization).
• Collaborates on the development of semi-autonomous workflows or Agentic AI systems, focusing on tool integration, robust error handling for non-deterministic LLM outputs, and latency management.
• Integrates advanced Azure AI services and LLM workflows with existing enterprise Angular front-ends and Java/Spring Boot back-ends, ensuring secure data transit, state management, and seamless UI/UX for AI-driven features.
• Supports change readiness initiatives as needed.

Job Level Specifications

• Extensive knowledge and application of full-stack engineering principles, theories, and concepts.
• Complete knowledge of all job functions and broad industry best practices, techniques, and standards regarding cloud-native development and enterprise AI deployment.
• Determines the best approach to achieve results and provides suggestions to improve policies, procedures, and system performance.
• Exercises considerable latitude in determining objectives and approaches to assignments.
• Works independently and requires the exercise of judgment and discretion.

Qualifications

• Typically 8+ years with bachelor's or equivalent.
• Bachelor's degree or equivalent experience from which comparable knowledge and job skills can be obtained.
• Azure AI Ecosystem: Hands-on experience navigating Azure AI Foundry / Azure AI Studio to configure hubs, deploy foundation models (e.g., GPT series, Llama), build prompt flows, and integrate vector search.
• Core Languages & Frameworks: Building and coding applications using languages/technologies such as Java, Python, Spring / Spring Boot, Web Services, and SQL.
• Frontend Technologies: Production-level experience using Angular, TypeScript, HTML, CSS/Sass, and Bootstrap.
• RAG & Agentic Patterns: Understanding of strategic document chunking, embedding selection, vector stores (e.g., Azure AI Search, pgvector), and exposure to multi-turn agent frameworks or semantic orchestration patterns.
• Emerging AI Standards (Preferred): Familiarity with the Model Context Protocol (MCP) for building secure, standardized connections between LLM applications, enterprise data sources, and business tools/APIs.
• Database & Data Management: Practical knowledge of SQL database design, optimization, and complex dataset retrieval. Familiarity with cloud data tools like Databricks is a plus.
• Testing & Evaluation: Experience utilizing testing tools like JUnit for Java, coupled with an awareness of LLM evaluation practices (assessing grounding, relevance, and latency metrics).
• CI/CD & DevOps: Experience participating in automated deployment and integration pipelines using GitHub Actions, Jenkins, Maven, or Gradle.
• Methodologies: Strong alignment with SDLC practices including Agile/Scrum and structured configuration environments.