Nashville, Tennessee
Job Description
The Senior AI Engineer is responsible for designing, developing, deploying, and continuously improving production AI agents and agentic workflows that support attorneys, practice groups, and Firmwide Department teams across the Firm. This role translates complex business needs into reliable AI enabled solutions that combine language models, enterprise tools, governed data, workflow automation, and appropriate human review. Working primarily within Azure Databricks and/or Azure AI Studio with native Azure services, the Senior AI Engineer will develop solutions using technologies such as n8n, LangGraph, Python, and enterprise APIs. The position will maintain a strong hands-on development focus while also providing technical guidance, code review, mentoring, and training to AI Engineers and helping establish engineering standards and best practices. The Senior AI Engineer will partner closely with the department leadership, AI Engineers, Data Architects, DevOps teams, and business stakeholders to deliver secure, scalable, and production ready AI solutions.
KEY RESPONSIBILITIES
Partner with attorneys, practice groups, and Firmwide teams to understand existing workflows, identify opportunities for AI-enabled solutions, and define technical requirements, acceptance criteria, and appropriate boundaries for automation and human review.
Design, build, deploy, and maintain production AI agents and agentic workflows using n8n, LangGraph, Python, enterprise APIs, and related technologies.
Develop n8n workflows incorporating triggers, webhooks, AI Agent nodes, enterprise connectors, reusable sub-workflows, custom code, secure credential management, and appropriate error handling.
Build stateful LangGraph workflows incorporating tool calling, conditional routing, checkpoints, persistent state, stopping conditions, recovery mechanisms, and human approval steps.
Design prompts, context assembly, structured outputs, retrieval-augmented generation (RAG), and model selection strategies based on defined quality, latency, cost, and business requirements.
Develop reusable agent tools, APIs, and integrations that enable agents to retrieve information and perform authorized actions securely across enterprise systems.
Partner with the Data Architect to implement governed and permission-aware data access, retrieval, persistent state, and memory while maintaining source traceability and appropriate user and matter-level access controls.
Develop evaluation datasets, automated tests, and regression testing processes in partnership with domain experts to assess agent behavior, retrieval quality, tool-use accuracy, task completion, groundedness, latency, and cost.
Analyze agent traces, tool calls, workflow outcomes, and failure patterns using evaluation and observability tools to troubleshoot issues and continuously improve solution performance.
Incorporate appropriate security and reliability controls, including input and output validation, authorization, credential protection, retries, timeouts, stopping conditions, recovery procedures, and escalation paths.
Test solutions for risks such as prompt injection, inappropriate tool use, unauthorized data access, and data leakage, incorporating human approval for consequential actions where appropriate.
Partner with DevOps teams to support application deployment, environment configuration, secrets management, monitoring, release management, observability, and rollback processes across Azure environments.
Maintain ownership of the production performance, troubleshooting, maintenance, and continuous improvement of assigned AI agents and workflows.
Mentor and train AI Engineers through technical guidance, pairing, code reviews, and practical learning sessions.
Establish, document, and promote engineering best practices related to agent architecture, workflow development, evaluation, testing, security, observability, and production delivery.
REQUIRED EDUCATION, KNOWLEDGE &…