AI Data & Integration Engineer

Freshfields Bruckhaus Deringer•Raleigh, NC

7d•$70,000 - $170,000

About The Position

Lead the design and delivery of secure, reliable, and scalable data foundations and integrations for AI applications across a global law firm. The role enables AI assistants, enterprise search, and agent workflows to use legal and business information with appropriate context, permissions, quality, and traceability. The engineer owns the architecture and technical design of data pipelines and secure API integrations connecting document management, knowledge, client, matter, and business systems to AI platforms and agent tools. The role establishes reusable patterns and standards for ingestion, transformation, indexing, access, synchronization, authentication, authorization, versioning, error handling, observability, rate limiting, and resilient data exchange. The role preserves source permissions, information barriers, and client restrictions throughout processing and retrieval; propagates updates, permission changes, retention decisions, and deletions to downstream systems; and develops consistent data models, governed data products, APIs, and tool interfaces. Working with AI evaluation engineers, the engineer converts data, retrieval, and integration failure modes into tests and production controls, diagnoses complex production issues, and leads technical delivery across internal teams and external partners. Responsibilities also include architecture and code review, mentoring engineers, managing dependencies, and translating business, legal, and governance requirements into delivery plans.

Requirements

Strong experience managing or leading teams that design, deliver, and operate production application or 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 and implement granular access controls and information barriers.
Proficiency in using coding agents to accelerate design, development, testing, and troubleshooting, with sound judgment 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.
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 of five years in data engineering, integration engineering, or a closely related discipline.

Nice To Haves

Legal or professional services experience.
Familiarity with iManage or comparable document management systems.
Familiarity with entity resolution, legal taxonomies, knowledge graphs, and governed agent integrations such as MCP.

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 data 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.
Use evaluation results to improve data and API design.
Diagnose complex production issues across source systems, APIs, data pipelines, indexes, and consuming AI applications while 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.