Engineer Lead

Law School Admission Council•Newtown Township, PA

1d•$126,000 - $135,000•Remote

About The Position

LSAC’s mission is to advance law and justice by promoting access, equity, and fairness in law school admission and supporting the learning journey from prelaw through practice. Pay rate: $126,000 to $135,000 per year, depending on experience We are seeking an Engineer Lead with Generative AI experience. You will work as part of an autonomous agile team to develop features and applications to meet the needs of the Technology Solutions and Platforms department. We are looking for a candidate with a deep understanding of engineering best practices and a strong agile mindset. As lead, you are responsible for ensuring the team's alignment with best practices, enterprise architecture, and stakeholder satisfaction. In addition, this position is responsible for operational maintenance, troubleshooting, and support of applicable back-end systems. We rely heavily on web-based services, and you will have the opportunity to work with new and interesting technology. This role carries an expanded mandate to design, prototype, and deliver Generative AI solutions that directly enhance LSAC products and internal tooling. LSAC currently uses GitHub Copilot as its primary AI-assisted development tool; this role will own that practice and grow it—expanding our AI capabilities into large language models (LLMs), retrieval-augmented generation (RAG), agentic workflows, and responsible AI practices. You will partner with the Software Development Manager (SDM), data engineers, and stakeholders to identify high-value AI opportunities and bring them to production. Equally critical is the human side of this transformation. This person will be a hands-on change agent for Generative AI adoption across the engineering organization—translating emerging AI capabilities into practical, day-to-day habits for developers, testers, and product teams. You will design and run enablement programs, lead by example in code, and build the organization's confidence and fluency with AI tooling from the ground up.

Requirements

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.
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.
5–10 years of experience in full stack software engineering.
A. or B.S. degree in Computer Science, Software Engineering, or related field.

Nice To Haves

(Preferred) Experience with Selenium or comparable automated testing frameworks.
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.
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.

Responsibilities

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.