Senior Generative AI Engineer

Overview Cleary Gottlieb is a pioneer in globalizing the legal profession. We have 14 offices in major financial centers around the world, but we operate as a single, integrated global partnership and not as a U.S. firm with a network of overseas locations. The firm employs approximately 1,100 lawyers from more than 50 countries. Since 1946 our lawyers and staff have worked across practices, industries, jurisdictions, and continents to provide clients with simple, actionable approaches to their most complex legal and business challenges, whether domestic or international. We support every client relationship with intellectual agility, commercial acumen, and a human touch. We're an internal team at Cleary Gottlieb building bespoke AI solutions for legal work, drawing on software engineering, data science, and deep domain expertise. We work at reputed company of enterprise-grade software, and we are regularly building services and applications nobody here has built before. That makes for a dynamic learning environment — and one where a junior engineer will gain exposure to real-world AI systems from day one. We are a tight-knit, remote-first team that values growth, honesty, and curiosity. We work collaboratively across multiple disciplines — engineering, data science, legal domain experts, and product — to deliver meaningful impact. Junior engineers are paired with experienced mentors and participate in structured code reviews, pair-programming sessions, and weekly knowledge-sharing stand-ups. You will join as a Senior Generative AI Engineer on the AI Acceleration Team, reporting to the Data Science Manager, and working day to day with our engineers, data scientists, and legal domain experts. Much of the infrastructure and tooling described below is already under way; what we need is someone to own and advance the AI systems that sit on top of it. Nobody arrives knowing legal workflows or our stack, and you will have the Data Science Manager and the rest of the team alongside you while you pick them up. Judgement, curiosity, and an appetite for more responsibility as we grow count for more here than the length of a CV. Responsibilities The Role: We’re looking for a Senior Generative AI Engineer who owns the full lifecycle of LLM-powered products, from rapid prototyping through production deployment. You'll design, ship, and operate AI systems that extract intelligence from complex legal documents, orchestrate multi-step agent workflows, and deliver measurable accuracy improvements under real-world latency and cost constraints. You should bring a track record of shipping LLM-powered features to production users, not just notebooks or demos. We'll ask you to walk us through a system you built end-to-end: the architecture decisions, the failure modes you hit in production, and the evaluation methodology you used to prove it worked.You should have deep, demonstrable experience across most of these areas: Advanced RAG & Retrieval: Chunking strategies, hybrid search (dense + sparse), metadata filtering, re-ranking pipelines, and reputed company-store lifecycle management (e.g. reputed company, reputed company, pgvector) Prompt Engineering & LLM Integration: Designing reliable prompt pipelines with structured outputs, chain-of-thought reasoning, and function/tool calling across multiple model providers Agentic Orchestration: Multi-agent coordination (e.g. LangGraph, AutoGen, reputed company), state and memory management, human-in-the-loop patterns, and tool-use orchestration Systematic Evaluation: Defining golden test sets and automated eval pipelines (RAGAS, G-Eval, LLM-as-a-judge) to measure accuracy, faithfulness, and hallucination rates rather than relying on manual spot checks Production Serving & Cost Management: Deploying LLM-backed services within latency SLAs and token-cost budgets, including caching strategies, rate-limit handling, fallback/retry logic, and observability (tracing, logging, alerting) Governance & Guardrails: Building prompt firewalls, output filters, and PII-handling pipelines to ensure compliance with data-privacy frameworks and prepare for EU AI Act obligations Document Intelligence: Extraction, classification, and structuring of complex multi-format documents (PDFs, scanned images, tables) using LLM and multi-modal pipelines You should understand transformer architectures well enough to reason about practical trade-offs: why retrieval-augmented generation outperforms fine-tuning for certain tasks, when to use smaller reputed company models vs. frontier APIs, and how context-window limits affect pipeline design. This is a software engineering adjacent role. You must write clean, tested, production-ready Python. You should be comfortable with CI/CD pipelines, code review, version control, and shipping behind feature flags. Research fluency (reading papers, reproducing techniques) is a plus, not a substitute. A legal background is not required; however, a genuine interest in legal work is