At Fried Frank, we’re a community of 800 lawyers and 500 business services professionals across New York, Washington, DC, London, Frankfurt, and Brussels. We advise leading corporations, investment funds, and financial institutions on high-stakes M&A, securities, regulatory matters, real estate, and litigation. Our culture is grounded in our core values — excellence, integrity and collaboration — and is designed to foster continuous learning, meaningful mentorship, and lasting professional growth. We are firmly committed to pro bono service and social justice, building on a proud legacy in civil rights. Our inclusive talent strategy is a core part of our broader talent management efforts and we remain steadfast in fostering a workplace where everyone has the opportunity to grow, thrive, and become their best professional and personal selves. Our business services professionals are integral to the firm’s success, driving innovation, operational excellence and exceptional client service across all areas of the firm. We offer competitive compensation and a comprehensive benefits package, including comprehensive medical coverage, retirement plans and health and wellness initiatives designed to support your personal and professional wellbeing. We welcome passionate, driven individuals to join us, and be part of a team where you’ll be supported, inspired and empowered to build an exceptional career.
Position Summary:
We are seeking an AI Engineer with experience building AI-driven applications, including RAG pipelines and LLM-powered systems. This role focuses on implementing and improving well-defined features within existing AI systems, contributing to design, experimentation, and production deployment.
This role sits within Practice Innovation and collaborates closely with Legal Engineering, Practice Solutions, Knowledge Management, Cloud/Infrastructure teams, and other stakeholders across the firm.
Duties & Responsibilities:
• Design, build, and enhance retrieval-based AI systems, data pipelines, and workflow-driven applications.
• Develop and optimize AI agents that orchestrate multi-step legal workflows and tool integrations.
• Apply modern RAG (Retrieval-Augmented Generation) design patterns to improve system accuracy, reliability, and performance.
• Conduct structured experimentation and evaluation to refine prompts, retrieval strategies, agent behavior, and model outputs.
• Contribute to data ingestion, cleaning, structuring, and indexing processes supporting AI applications.
• Support deployment, monitoring, and continuous improvement of AI tools in production environments.
• Take ownership of well-defined features and contribute to system enhancements from design through production.
• Partner with Cloud, Networking, and Infrastructure teams to ensure secure, scalable, and reliable deployments.
Education / Experience:
• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 2+ years of professional experience in software engineering, machine learning, or AI-related roles.
Skills & Abilities:
• Hands-on experience building LLM-based applications in production or near-production environments.
• Experience with vector databases and similarity search (e.g., Qdrant, Pinecone, Weaviate, or similar).
• Solid understanding of RAG architectures and retrieval optimization.
• Experience working with structured and unstructured data.
• Strong proficiency in Python and software engineering fundamentals (clean code, testing, system design).
• Familiarity with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes).
• Strong analytical and problem-solving skills.
• Ability to operate independently on well-scoped problems and escalate appropriately when needed.
• Strong communication skills with the ability to work effectively across technical and non-technical audiences.
Preferred Qualifications
• Experience deploying AI agents or LLM-powered systems in production environments.
• Experience with Azure cloud services (strong plus).
• Familiarity with monitoring, logging, and evaluation practices for production AI systems.
• Experience working in enterprise or regulated environments.