Job Description
About Kirkland & Ellis At Kirkland & Ellis, we don’t just meet the standard for legal excellence — we set it. Our culture is built on teamwork, ingenuity and an unwavering commitment to continuous growth. We tackle the most sophisticated legal challenges with bold ideas and innovative solutions, powered by the exceptional experience and ambition of our 7,000+ people, including 4,000+ attorneys, across 23 offices worldwide. Our dedicated professionals share our lawyers’ commitment to excellence and show up each day to do meaningful work that helps drive global business, investment and innovation forward. What You’ll Do Are you energized by building enterprise-grade AI solutions that solve complex, high-impact challenges in a sophisticated professional services environment? As an Innovation AI Developer at Kirkland & Ellis, you will play a pivotal role on our Innovation Engineering team—designing and deploying intelligent, secure, and scalable AI applications that transform how we deliver legal services and operate our business. In this role, you’ll partner closely with attorneys, business leaders, data scientists, and infrastructure teams to translate nuanced legal and operational challenges into practical AI solutions with measurable outcomes. From early-stage use case discovery through production deployment and monitoring, you’ll work across the full AI lifecycle—leveraging large language models (LLMs), retrieval-augmented generation (RAG), classical machine learning, and modern cloud architectures to drive efficiency, quality, and informed decision-making. This is an opportunity to shape enterprise-grade AI capabilities at one of the world’s leading law firms, working at the intersection of advanced engineering and high-impact legal work. AI Solution Architecture & Development – Collaborate with legal and business stakeholders to identify high-value AI use cases, define measurable success criteria (e.g., efficiency, quality, risk reduction), and architect scalable solutions using Python and modern AI frameworks such as OpenAI, Azure OpenAI, Hugging Face, LangChain, and LangFlow. Retrieval-Augmented Generation (RAG) & Intelligent Systems – Design, build, and maintain RAG pipelines using vector databases and knowledge graph solutions. Develop AI agents that automate multi-step workflows and enable sophisticated task orchestration. Reusable Engineering & Scalable Innovation – Create reusable libraries, patterns, and documentation that support long-term innovation and maintainability across the firm’s AI ecosystem. Evaluation & Model Governance – Design and maintain LLM evaluation frameworks to measure accuracy, robustness, safety, and alignment to use-case objectives. Ensure AI systems meet firm security, privacy, and governance standards. Production Deployment & Infrastructure Enablement – Support continuous integration and continuous delivery (CI/CD) workflows, containerization (Docker), and orchestration (Kubernetes) to ensure production readiness. Partner with infrastructure and security teams to support hybrid cloud and on-premise deployments in sensitive data environments. Stakeholder Partnership & AI Enablement – Serve as a subject matter resource for attorneys and business teams, advising on prompt engineering, model selection, and architecture decisions. Support pilot implementations, user training, and feedback loops to drive adoption and measurable impact. Emerging Technology & Strategy Contribution – Evaluate new tools, open-source libraries, and vendor platforms to inform firm AI strategy. Contribute to internal education and thought leadership on AI best practices. What You’ll Bring Education – Bachelor’s degree in computer science or a related field preferred. Experience – 7+ years of experience in professional services, legal, or technical environments, with demonstrated hands-on experience building and deploying AI applications. AI Engineering Expertise – Strong Python development skills and experience designing and implementing RAG pipelines, working with vector databases (e.g., Pinecone), and building AI agents and multi-service AI architectures—particularly within Azure environments. Cloud & DevOps Proficiency – Familiarity with Azure services (e.g., Cognitive Search, Cosmos DB, AI Studio), as well as Docker, Kubernetes, and CI/CD practices that support scalable, production-ready deployments. Evaluation & Data Integration – Experience designing and maintaining LLM evaluation frameworks (e.g., Promptfoo, OpenAI Evals, LangSmith) and integrating structured and unstructured data sources into AI systems. Security & Governance Awareness – Experience operating within environments that require strict adherence to security, privacy, an