Lead Engineer

Role Overview

• Collaborate with partners within the company to design, configure, maintain, and promote a variety of internally
• and externally facing applications.
• Collaborate across areas to ensure application reliability and coding to architectural standards.
• Support product teams by advocating for their needs and providing constructive guidance.
• Provide the glue across dependent teams to ensure technical dependencies are planned for and addressed in a fast-paced environment.
• Connect with developers within your team to coach, inspire, and foster a continuous learning environment.
• Build and maintain scalable full-stack applications.
• Continuously monitor, test, and optimize software.
• Collaborate with software engineers, the Software Development Manager (SDM), analysts, and stakeholders to deliver solutions that meet or exceed customer expectations.
• Contribute as part of the team by leveraging continuous delivery methods and test-driven development to frequently deliver new functionality.
• Deliver high-quality code and hold the team to the same standard through code review and mentoring.
• Work to scale software to support dynamic teams in a fast-paced environment.
• Be a contributor of knowledge to the team by reviewing code, sharing experience, and listening.
• Champion and govern team-wide use of GitHub Copilot – establish prompting standards, review AI-generated code for security and quality, track productivity metrics, and coach developers on effective, responsible use of AI-assisted development.
• Drive organizational Gen-AI adoption: build and execute a structured enablement roadmap that moves the engineering organization from awareness to competency, delivering workshops lunch-and-learns, hands-on labs, and reference implementations that make AI tooling tangible and accessible for developers at all levels.
• Coach and mentor engineers on Gen-AI practices, provide 1:1 and team-level guidance on integrating AI into daily workflows including AI-assisted coding, AI-augmented code review, AI-accelerated test generation (unit, integration, regression), and effective use of AI in debugging and documentation.
• Help engineers build critical evaluation skills for vetting AI-generated output.
• Act as internal AI change agent.
• Identify adoption blockers, measure team AI maturity over time, gather feedback from engineers and stakeholders, and continuously iterate on enablement programs; evangelize Gen-AI success stories across the broader Technology Solutions and Platforms organization.
• Architect and implement Generative AI features using Azure OpenAI Service, OpenAI APIs, and open-source LLMs including chat interfaces, document summarization, intelligent search, and automated content generation.
• Design and maintain Retrieval-Augmented Generation (RAG) pipelines integrating vector databases (e.g., Azure AI Search, Pinecone, pgvector) with enterprise data sources to ground model outputs in authoritative content.
• Lead prompt engineering efforts; craft, version, evaluate, and iteratively refine system prompts, few-shot examples, and chain-of-thought strategies to maximize accuracy and safety.
• Establish and enforce responsible AI practices, bias evaluation, content filtering, PII redaction, audit logging, and compliance with LSAC data governance standards for all AI-powered features.

Requirements

• 5–10 years of experience in full stack software engineering
• A. or B.S. degree in Computer Science, Software Engineering, or related field
• Master's degree preferred
• Azure certifications (e.g., AZ-204, AZ-400, AZ-305) preferred
• Azure AI certifications – AI-102 (Azure AI Engineer Associate) and/or Microsoft certified, Azure OpenAI certification or equivalent demonstrated proficiency preferred
• Coursework, certifications, or verifiable project experience in machine learning fundamentals, natural language processing, or applied AI/ML (e.g., DeepLearning.AI, fast.ai, Coursera ML Specialization) preferred
• Strong written and verbal communication skills, with experience using MS Teams
• Holds a strong sense of accountability for both individual and team objectives
• Embraces a forward-thinking mindset, contributing to a culture of continuous improvement and creativity
• Excellent time management, prioritization, attention to detail, and organizational skills
• Ability to listen to stakeholders and form solutions
• Proven ability as a servant leader
• Experience with relational and unstructured data repositories; specifically, strong knowledge of stored procedures, scripts, Cosmos DB, SQL, and Oracle
• Experience with web and cross-platform technologies including Web Services, React JS, MS VB, and C# .NET frameworks (Web Forms, Windows Forms, .NET Web API, Entity Framework 6.4+, MVC, SPA)
• Experience with a variety of object-oriented languages
• Experience with Git, code management methods, CI/CD pipelines, and Azure DevOps
• Familiarity with RESTful or web APIs
• Familiarity with Web Content Accessibility Guidelines (WCAG) 2.1 and ARIA standards
• Azure tech stack knowledge (Azure Functions, Azure Data Factory, Azure Storage Account, Azure Key Vault, Azure Cosmos DB, Service Bus, Azure App Service, Azure VMs, Azure Table Storage)
• Knowledge of modern development practices and the development lifecycle with experience using Scrum, Kanban, Lean, or other agile methodologies
• Values the success of the team over personal objectives
• (Preferred) Experience with Selenium or comparable automated testing frameworks
• Gen-AI change leadership required
• Demonstrated track record of driving Generative AI adoption across an engineering team or organization
• Designing enablement programs, communicating the value of AI tools to skeptical stakeholders, measuring adoption metrics, and iterating based on feedback
• Able to translate hype into pragmatic, incremental change
• AI coaching and mentoring experience required
• Proven ability to uplift peers and direct-reports on Gen-AI practices
• Covering AI-assisted coding, prompt design, AI-augmented testing (unit, integration, and regression test generation), AI-assisted debugging, and responsible use of AI-generated code
• Can assess individual and team AI maturity and tailor coaching accordingly
• GitHub Copilot experience required
• Demonstrated, production-level proficiency with GitHub Copilot in VS code or JetBrains IDEs, experience writing effective inline prompts, using Copilot Chat, and enforcing code review standards for AI-generated output
• Ability to configure and manage Copilot at the organization/repository level via GitHub Enterprise settings
• Generative AI/LLM expertise
• Hands on experience with Azure OpenAI Service (GPT-4o, GPT-4, Assistants API), OpenAI API, and/or open-source LLMs (Llama, Mistral, Phi)
• Familiarity with LangChain, Semantic Kernel, or comparable orchestration frameworks
• Vector search & RAG – practical knowledge of embedding models, chunking strategies, hybrid search, and re-ranking techniques, experience with Azure AI Search, Pinecone, Chroma, or pgvector
• Prompt engineering & evaluation – ability to design structured prompts, implement few-shot and chain-of-thought patterns, and use evaluation frameworks (Azure AI Evaluation, Promptflow, Ragas, or similar) to measure quality and regression
• Responsible AI & AI governance – understanding of content safety controls, PII handling in LLM contexts, model fairness, explainability, and Microsoft Responsible AI principles
• Experience with agentic AI patterns (AutoGen, Azure AI Agent Search, tool-calling, multi-agent orchestration) preferred
• Familiarity with MLOps/LLMOps practices, fine-tuning pipelines, model versioning, deployment monitoring, and drift detection preferred

Tech Stack

• Azure
• Entity Framework
• JavaScript
• Oracle
• React
• Selenium
• SQL
• Vault
• .NET