AI Engineer - Philadelphia

Dechert LLP is a global specialist law firm focused on high end legal work. The AI Engineer is embedded within the Dechert Innovation Lab and works directly with attorneys, legal professionals, and business-services teams to discover, evaluate, prototype, and pilot emerging AI-enabled technologies that may improve the delivery of legal services and firm operations. This role operates at the intersection of technology scouting, rapid experimentation, artificial intelligence, automation, and enterprise innovation. The role translates complex legal and operational needs into testable prototypes and proof-of-concept solutions—including generative AI applications, retrieval-augmented generation (RAG) solutions, workflow automations, intelligent agents, integrations, and custom web applications—to assess feasibility, value, and readiness for broader adoption. The position is part of the Dechert Innovation Lab's rapid-experimentation function and is responsible for moving high-value opportunities from idea through proof of concept and pilot, evaluating outcomes, and providing recommendations on whether and how to transition successful experiments to the appropriate enterprise applications, platform, or operations team for production deployment and long-term support. ESSENTIAL JOB FUNCTIONS: • Partner with attorneys, practice groups, legal project management, knowledge management, finance, risk, client development, and business-services teams to identify innovation opportunities, understand workflows, pain points, and desired outcomes. • Facilitate technical discovery and innovation sessions; assess business problems for AI, automation and emerging technologies. • Rapidly design, develop, and evaluate proof-of-concept and pilot solutions using approved and emerging AI platforms, APIs, low-code tools, workflow automation platforms, and custom development technologies. • Build and test experimental AI-enabled applications, including generative AI assistants, document and knowledge-search solutions, RAG applications, workflow copilots, intelligent agents, and decision-support tools. • Evaluate and select appropriate technical approaches based on innovation potential, business value, solution complexity, data sensitivity, scalability, supportability, and time-to-value. • Experiment with solution success measures, such as feasibility, time saved, adoption potential, accuracy, user satisfaction, process-cycle reduction, risk reduction, and business impact. • Evaluate pilot outcomes and recommend whether to transition successful experiments to the appropriate enterprise application teams. • Relentlessly scout and stay ahead of the curve on AI engineering practices, legal-industry AI use cases, emerging tools and platforms, AI governance requirements, and applicable technology trends; recommend new technologies for future evaluation. • Participate in intake prioritization, solution estimation, innovation pipeline planning, vendor evaluations, and portfolio reporting. • Other responsibilities as needed. KNOWLEDGE: • Experience with Generative AI, Model Context Protocol (MCP), Azure/OpenAI, Large Language Model (LLM), Claude, 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. • Automation and orchestration technologies, such as workflow platforms, robotic process automation, low-code/no-code development tools, and integration platforms. • Software development methodologies, including Agile, Kanban, rapid prototyping, product discovery, and iterative delivery. SKILLS: • Strong software engineering skills in one or more modern programming languages, such as Python, JavaScript/TypeScript, C#, ASP.NET Core, SQL, or similar technologies. • Ability to build and deploy AI-enabled applications using APIs, SDKs, orchestration frameworks, cloud services, and enterprise platforms. • Ability to translate ambiguous business needs into testable hypotheses and experiment designs. • Strong consultative and communication skills, with the ability to work effectively with attorneys, business leaders, technical teams, vendors, and nontechnical users. • Strong analytical, problem-solving, and systems-thinking abilities. • Ability to balance speed, experimentation, and learning with security, quality, governance, maintainability, and long-term supportability. • Experience designing user-centered solutions and incorporating feedback into rapid experiment cycles and iterations. • Comfort with ambiguity in a fast-moving environment with multiple concurrent initiatives. • Resilience and persistence in iterative experimentation, including the ability to learn from unsuccessful outcomes, adapt approaches, and maintain momentum through successive testing cycles. INTEREST