AI Engineer

Dechert, LLP is a global specialist law firm focused on high end legal work. The AI Engineer partners directly with attorneys, legal professionals, and business-services teams to identify, design, build, and deploy secure AI-enabled solutions that improve the delivery of legal services and firm operations. This role operates at the intersection of business discovery, rapid application development, artificial intelligence, automation, and enterprise technology. The role translates complex legal and operational needs into practical, scalable solutions—including generative AI applications, retrieval-augmented generation (RAG) solutions, workflow automations, intelligent agents, integrations, and custom web applications. The position is part of the IT Applications rapid-development function and is responsible for moving high-value opportunities from idea through proof of concept, pilot, production readiness, and transition to the appropriate enterprise applications, platform, or operations team for long-term support. The engineer works within the firm's security, privacy, data governance, architecture, and AI-risk-management standards. Job Description ESSENTIAL JOB FUNCTIONS: • Partner with attorneys, practice groups, legal project management, knowledge management, finance, risk, client development, and business-services teams to understand workflows, pain points, and desired outcomes. • Facilitate technical discovery sessions; assess business problems for AI, automation, workflow, integration, and custom application opportunities. • Rapidly design and develop proof-of-concept and pilot solutions using approved AI platforms, APIs, low-code tools, workflow automation platforms, and custom development technologies. • Build secure AI-enabled applications, including generative AI assistants, document and knowledge-search solutions, RAG applications, workflow copilots, intelligent agents, and decision-support tools. • Develop and maintain integrations among firm applications, document repositories, knowledge systems, data platforms, collaboration tools, and approved third-party services. • Design AI solutions that appropriately address data classification, confidentiality, ethical use, model limitations, human review, auditability, access controls, retention, and regulatory obligations. • Work with enterprise solutions architecture, information security, privacy, records management, risk management, and legal teams to ensure solutions meet global firm standards before production deployment. • Evaluate and select appropriate technical approaches based on business value, solution complexity, data sensitivity, scalability, supportability, and time-to-value. • Develop reusable components, integration patterns, prompt libraries, evaluation methods, documentation, and technical standards that accelerate future AI and automation delivery. • Conduct testing, quality assurance, user acceptance testing, model evaluation, and performance monitoring for AI-enabled solutions. • Establish solution success measures, such as time saved, adoption, accuracy, user satisfaction, process-cycle reduction, risk reduction, and business impact. • Create technical documentation, architecture diagrams, support materials, operating procedures, and knowledge-transfer artifacts. • Transition mature solutions to the appropriate applications support and operations or enterprise application development, ensuring clear ownership, support readiness, and lifecycle plans. • Monitor deployed solutions for performance, reliability, adoption, model behavior, security concerns, and opportunities for iterative improvement. • Stay current on AI engineering practices, legal-industry AI use cases, emerging tools, AI governance requirements, and applicable technology trends. • Participate in intake prioritization, solution estimation, roadmap planning, vendor evaluations, and innovation portfolio reporting. • Partner with Legal, Risk, Security, and Compliance Teams to ensure AI initiatives meet applicable legal and regulatory obligations. • Ensure AI systems handling client or confidential information meet applicable privacy, security, and data handling requirements across all operating jurisdictions. • Other responsibilities as needed. Knowledge • Experience with Generative AI, Model Context Protocol (MCP), Azure/OpenAI, Large Language Model (LLM), Microsoft CoPilot, prompt engineering, retrieval-augmented generation, embeddings, vector databases, AI agents, model evaluation, and responsible AI practices. • Application development concepts, including APIs, microservices, web applications, databases, authentication, authorization, logging, monitoring, testing, and CI/CD practices. • Cloud and enterprise technology environments, including secure API integration, DevOps, identity and access management, data governance, and application lifecycle management. • Automation and orchestration technologies, such as workflow platforms, robotic process automation,