Senior AI Engineer

Join a firm that values secure, production-grade AI and delivers real workflow improvements for attorneys and Firmwide teams. This onsite role in Nashville, TN focuses on designing, developing, deploying, and continuously improving production AI agents and agentic workflows. You will build solutions primarily within Azure Databricks and/or Azure AI Studio, combining hands-on engineering with practical guidance for scalable, governed deployments.

Responsibilities

• Collaborate with attorneys, practice groups, and Firmwide teams to understand current workflows, define technical requirements and acceptance criteria, and establish 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.
• Create n8n workflows with triggers, webhooks, AI Agent nodes, enterprise connectors, reusable sub-workflows, custom code, secure credential management, and appropriate error handling.
• Build stateful LangGraph workflows with tool calling, conditional routing, checkpoints, persistent state, stopping conditions, recovery mechanisms, and human approval steps.
• Develop prompts, context assembly, structured outputs, retrieval-augmented generation (RAG), and model selection strategies aligned to quality, latency, cost, and business needs.
• Develop reusable agent tools, APIs, and integrations that let agents retrieve information and perform authorized actions securely across enterprise systems.
• Partner with the Data Architect to implement governed, permission-aware data access and retrieval, including persistent state and memory while maintaining source traceability and user and matter-level access controls.
• Build evaluation datasets and automated and regression testing processes with domain experts to measure agent behavior, retrieval quality, tool-use accuracy, task completion, groundedness, latency, and cost.
• Use evaluation and observability tooling to analyze agent traces, tool calls, workflow outcomes, and failure patterns to troubleshoot and continuously improve performance.
• Apply security and reliability controls across the stack, including validation, authorization, credential protection, retries, timeouts, stopping conditions, recovery procedures, and escalation paths.
• Test for risks such as prompt injection, inappropriate tool use, unauthorized data access, and data leakage, with human approval for consequential actions where appropriate.
• Work with DevOps to support deployment, environment configuration, secrets management, monitoring, release management, observability, and rollback across Azure environments.
• Maintain ownership of production performance, troubleshooting, maintenance, and continuous improvement for 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 for agent architecture, workflow development, evaluation, testing, security, observability, and production delivery.

Requirements

• Experience personally designing, developing, deploying, and supporting production AI agents, agentic applications, or tool-enabled LLM workflows.
• Strong Python skills and working knowledge of SQL, APIs, automated testing, version control, code review, and maintainable software integration practices.
• Hands-on experience with LangGraph (or comparable frameworks) for building stateful, multi-step AI agents.
• Hands-on experience with n8n (or comparable automation platforms), including the ability to combine workflow automation with custom agent services.
• Hands-on experience delivering AI solutions using Azure Databricks, Azure AI Studio, and/or native Microsoft Azure services, including model access, application deployment, and integration with governed enterprise data.
• Strong understanding of agent architecture and workflow design, including tool schemas, prompt and context management, structured outputs, state transitions, memory, checkpointing, stopping conditions, and human review.
• Experience designing API and webhook integrations and asynchronous execution, including authentication, rate limits, retries, error handling, and idempotency.
• Experience implementing RAG and governed data access, including retrieval filtering, source permissions, citations, and validation of retrieved information.
• Experience developing evaluation datasets, automated tests, regression testing, and using agent traces and failure analysis to improve performance.
• Knowledge of AI application security and reliability practices, including authorization, credential protection, sensitive information handling, constrained tool access, recoverable workflows, and human controls.
• Experience with production software delivery practices, including versioning, environment configuration, automated testing, observability, monitoring, and rollback procedures.
• Ability to translate ambiguous or complex business requirements into practical technical solutions and clearly communicate tradeoffs to technical and non-technical stakeholders.
• Ability to mentor and train engineers, conduct effective code reviews, and contribute to technical standards while maintaining significant hands-on development responsibilities.

Technologies

• Azure Databricks, Azure AI Studio, Azure
• n8n, LangGraph, Python, SQL, MLflow, LangSmith
• Model Context Protocol (MCP), JavaScript, TypeScript

Preferred Skills & Knowledge

• Production experience using both n8n and LangGraph, especially solutions combining workflow automation with custom Python-based agent services.
• Experience deploying agents through Azure Databricks and using Lakebase (or similar) technologies for persistent agent state, checkpoints, and memory.
• Experience with AI evaluation and observability tools such as MLflow, LangSmith, or comparable platforms.
• Experience developing MCP servers, reusable agent tools, or integrations connecting AI solutions with enterprise systems and governed data.
• Experience with multi-agent architectures, model routing, advanced retrieval techniques, and other agentic design patterns.
• Working knowledge of JavaScript or TypeScript for custom integrations and lightweight application interfaces.
• Experience developing user interfaces that show agent progress, source citations, approval workflows, and user feedback.
• Experience delivering AI or technology solutions directly with business users in legal services, professional services, or other sensitive-information environments with complex workflows.

Physical Requirements

• Ability to sit and stand for extended periods.
• Ability to lift up to 15 pounds.

Salary: USD 170,000 - 200,000 per year.

Location: Nashville, TN (onsite).