Lead Engineer
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Job Description
Engineer Lead building Generative AI, RAG, and full-stack applications for LSAC, advancing access and fairness in law school admission. Leading Copilot adoption, AI governance, and engineering enablement.
Responsibilities:
- Collaborate with company partners to design, configure, maintain, and promote internal and external applications
- Ensure application reliability and adherence to architectural standards
- Support product teams by advocating for their needs and providing constructive guidance
- Coordinate technical dependencies across teams
- Coach, inspire, and foster continuous learning among developers
- Build and maintain scalable full-stack applications
- Monitor, test, and optimize software
- Collaborate with software engineers, the Software Development Manager, analysts, and stakeholders to deliver customer-focused solutions
- Use continuous delivery and test-driven development to frequently deliver functionality
- Produce high-quality code; conduct code reviews and mentor team members
- Scale software for dynamic teams in a fast-paced environment
- Share technical knowledge and experience with the team
- Govern team-wide GitHub Copilot use, including prompting standards, AI-generated code security and quality review, productivity metrics, and developer coaching
- Build and execute a Generative AI enablement roadmap through workshops, lunch-and-learns, hands-on labs, and reference implementations
- Coach engineers on AI-assisted coding, code review, test generation, debugging, documentation, and evaluation of AI-generated output
- Identify AI adoption blockers, measure maturity, gather feedback, iterate enablement programs, and share success stories
- Architect and implement Generative AI features using Azure OpenAI Service, OpenAI APIs, and open-source LLMs
- Design and maintain RAG pipelines using vector databases such as Azure AI Search, Pinecone, and pgvector
- Lead prompt engineering, including system prompts, few-shot examples, chain-of-thought strategies, versioning, and evaluation
- Establish responsible AI practices covering bias evaluation, content filtering, PII redaction, audit logging, and data governance compliance
Requirements:
- 5–10 years of experience in full stack software engineering
- B.A. or B.S. degree in Computer Science, Software Engineering, or related field
- Strong knowledge of stored procedures, scripts, Cosmos DB, SQL, and Oracle
- Experience with Web Services, React JS, MS VB, and C# .NET frameworks, including Web Forms, Windows Forms, .NET Web API, Entity Framework 6.4+, MVC, and SPA
- Experience with object-oriented languages
- Experience with Git, code management methods, CI/CD pipelines, and Azure DevOps
- Familiarity with RESTful or web APIs
- Familiarity with WCAG 2.1 and ARIA standards
- Knowledge of Azure technologies including Azure Functions, Azure Data Factory, Azure Storage Account, Azure Key Vault, Azure Cosmos DB, Service Bus, Azure App Service, Azure VMs, and Azure Table Storage
- Experience with Scrum, Kanban, Lean, or other agile methodologies
- Gen-AI change leadership required, including driving adoption, designing enablement programs, measuring adoption, and iterating based on feedback
- AI coaching and mentoring experience required, including AI-assisted coding, prompt design, AI-augmented testing, debugging, and responsible use of AI-generated code
- Production-level GitHub Copilot proficiency in VS Code or JetBrains IDEs; experience with inline prompts, Copilot Chat, AI-generated code review standards, and GitHub Enterprise configuration
- Ability to design structured prompts, implement few-shot and chain-of-thought patterns, and use evaluation frameworks such as Azure AI Evaluation, Promptflow, or Ragas
- Understanding of responsible AI and AI governance, including content safety, PII handling, fairness, explainability, and Microsoft Responsible AI principles
- Strong written and verbal communication skills, including experience with MS Teams
- (Preferred) Experience with Selenium or comparable automated testing frameworks
- Experience with agentic AI patterns preferred
- Familiarity with MLOps/LLMOps, fine-tuning pipelines, model versioning, deployment monitoring, and drift detection preferred
- Master's degree preferred
- Azure certifications such as AZ-204, AZ-400, or AZ-305 preferred
- Azure AI certifications or equivalent demonstrated proficiency preferred
- Coursework, certifications, or verifiable project experience in machine learning, NLP, or applied AI/ML preferred
Benefits:
- Remote work arrangements considered; described as a remote position
- Standard business hours: Monday–Friday, 8:30 a.m.–4:45 p.m. ET
- No travel expected
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