AI Data & Integration Engineer

Role purpose
Lead the design and delivery of secure, reliable and scalable data foundations and integrations for AI applications across a global law firm. Enable AI assistants, enterprise search and agent workflows to use legal and business information with appropriate context, permissions, quality and traceability.

Key responsibilities

• Own the architecture and technical design of data pipelines connecting document management, knowledge, client, matter and business systems to AI platforms. Establish reusable patterns for ingestion, transformation, indexing, access and synchronization.
• Lead the design, implementation and operation of secure API integrations between enterprise systems, cloud services, AI platforms and agent tools. Define standards for authentication, authorization, versioning, error handling, observability, rate limiting and resilient data exchange.
• Ensure source permissions, information barriers and client restrictions remain effective throughout processing, integration and retrieval. Design mechanisms to propagate document updates, permission changes, retention decisions and deletions to downstream stores, indexes and consuming applications.
• Develop consistent data models linking clients, matters, people and documents. Provide governed data products, APIs and tool interfaces that enable AI applications and agents to retrieve and act on information safely.
• Partner with AI evaluation engineers to turn data, retrieval and integration failure modes into measurable tests, regression suites and production controls, and use evaluation results to improve data and API design.
• Diagnose complex production issues across source systems, APIs, data pipelines, indexes and consuming AI applications, balancing quality, security, performance, cost and maintainability.
• Lead technical delivery across internal teams and external partners. Review architecture and code, mentor engineers, manage technical dependencies and translate business, legal and governance requirements into practical delivery plans.

Required skills and experience

• Strong experience managing/leading teams designing, delivering and operating production application/data platforms and integrations.
• Deep practical experience designing and integrating RESTful and event-driven APIs, including authentication and authorization, API gateways, schema and contract management, versioning, retries, rate limits, error handling, observability and secure integration with enterprise and third-party systems.
• Strong conceptual understanding of large language models, including their capabilities, limitations and common failure modes, and how data quality, context, retrieval, permissions and integration design affect AI application performance and evaluation outcomes.
• Knowledge of unstructured document processing and retrieval-augmented generation, including search indexes, embeddings, hybrid retrieval, reranking and retrieval evaluation.
• Demonstrated ability to design secure data services, implement granular access controls and information barriers.
• Proficiency in using coding agents to accelerate design, development, testing and troubleshooting, with sound judgement about human review and best practices for prompting, context management, validation, security and responsible use of generated code.
• Experience developing software in programming languages such as Python, Java or C#, and applying sound software engineering practices to reusable libraries, services and integration components.
• Experience with SDLC methodologies and associated development, automated testing and CI/CD tools such as Git, Jira and Jenkins.
• Demonstrated ability to learn continuously in a rapidly evolving AI, data and integration landscape, critically assess new tools and techniques, and apply relevant advances pragmatically to improve the firm’s products, engineering practices and risk controls.
• Experience making architectural decisions, leading complex technical delivery, reviewing designs and code, and developing other engineers. Ability to explain technical choices clearly to business, legal, security and governance stakeholders.
• Minimum five or more years in data engineering, integration engineering or a closely related discipline.
• Legal or professional services experience is desirable, including familiarity with iManage or comparable document management systems, entity resolution, legal taxonomies, knowledge graphs and governed agent integrations such as MCP.

For individuals assigned and/or hired to work in New York and California, Freshfields is required by law to include a reasonable estimate of the compensation range for this role. This compensation range is specific to the States of New York and California and takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $70,000 to $170,000.