Senior AI Engineer

Pillsbury Winthrop Shaw Pittman LLP; Nashville (TN); On-site; USD 170,000 - 200,000; Full time

Job summary

Pillsbury Winthrop Shaw Pittman LLP in Nashville, TN is seeking a Senior AI Engineer to design, deploy, and maintain production AI agents and agentic workflows that support attorneys and Firmwide teams, primarily using Azure Databricks and Azure AI Studio. You will collaborate with attorneys and practice groups to define requirements and automation boundaries that include human review.

You will build and optimize workflows with n8n and LangGraph, create prompts and RAG strategies, and ensure

Qualifications

• Hands-on experience designing, deploying, and supporting production AI agents, agentic workflows, or tool-enabled LLM applications.
• Strong Python and SQL skills, APIs, automated testing, version control, and maintainable software integration practices.
• Hands-on experience with LangGraph or a comparable framework for stateful, multi-step AI agents.
• Hands-on experience with n8n or a comparable workflow automation platform, including building and supporting solutions that combine workflow automation with custom agent services.
• 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 integrations using APIs, webhooks, 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, and regression testing using agent traces and failure analysis.
• Understanding of AI application security and reliability practices, including authorization, credential protection, sensitive information handling, constrained tool access, recoverable workflows, and appropriate 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 communicate considerations and tradeoffs to both technical and non-technical stakeholders.
• Demonstrated ability to mentor and train engineers, conduct effective code reviews, and contribute to technical standards and best practices while maintaining significant hands-on engineering work.

Responsibilities

• Collaborate with attorneys, practice groups, and Firmwide teams to understand workflows and define requirements with automation boundaries.
• Design, build, deploy, and maintain production AI agents and agentic workflows using n8n, LangGraph, Python, APIs, and related tech.
• Develop n8n workflows with triggers, webhooks, AI Agent nodes, and secure credential management.
• Build LangGraph workflows with tool calls, routing, checkpoints, and human approval steps.
• Create prompts, context assembly, structured outputs, and retrieval-augmented generation strategies.
• Develop reusable agent tools, APIs, and integrations across enterprise systems.
• Partner with Data Architect to implement governed data access, persistence, and memory with controls.
• Develop evaluation datasets, automated tests, and regression testing procedures.
• Analyze agent traces and tool calls to troubleshoot and improve performance.
• Apply security controls including input/output validation, authorization, and credential protection.
• Test for risks like prompt injection, tool misuse, and data leakage with human review where appropriate.
• Support DevOps in deployment, environment configuration, monitoring, observability, and rollback across Azure environments.
• Maintain production performance and mentor AI Engineers through guidance and reviews.
• Establish and promote engineering best practices for agent architecture and production delivery.

Skills

Python

SQL

LangGraph

n8n

Azure Databricks

Azure AI Studio

Azure

RAG

MCP

LangSmith

MLflow

Tools

n8n

LangGraph

Azure Databricks

Azure AI Studio

Azure

MLflow

LangSmith

MCP

Job description

The Senior AI Engineer will design, develop, deploy, and continuously improve production AI agents and agentic workflows that support attorneys and Firmwide teams. The role is based onsite in Nashville, TN and works primarily in Azure Databricks and/or Azure AI Studio, focusing on secure, scalable, production-ready delivery.

Key Responsibilities

• Collaborate with attorneys, practice groups, and Firmwide teams to understand existing workflows and define technical requirements, acceptance criteria, and automation boundaries that include 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 using triggers, webhooks, AI Agent nodes, enterprise connectors, reusable sub-workflows, custom code, secure credential management, and error handling.
• Build stateful LangGraph workflows featuring tool calling, conditional routing, checkpoints, persistent state, stopping conditions, recovery mechanisms, and human approval steps.
• Create prompts, context assembly, structured outputs, retrieval-augmented generation (RAG), and model selection strategies aligned to defined quality, latency, and cost needs.
• Develop reusable agent tools, APIs, and integrations that allow agents to retrieve information and perform authorized actions securely across enterprise systems.
• Partner with the Data Architect to implement governed, permission-aware data access and retrieval, persistent state and memory, and source traceability with user and matter-level controls.
• In collaboration with domain experts, develop evaluation datasets, automated tests, and regression testing processes 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 tooling to troubleshoot issues and improve performance.
• Apply security and reliability controls including input and output 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, including human approval for consequential actions where appropriate.
• Partner 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 agents and workflows.
• Mentor AI Engineers through technical guidance, 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.

Required Qualifications

• Hands‑on experience 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 a comparable framework for stateful, multi‑step AI agents.
• Hands‑on experience with n8n or a comparable workflow automation platform, including building and supporting solutions that combine workflow automation with custom agent services.
• 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 integrations using APIs, webhooks, 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, and regression testing using agent traces and failure analysis.
• Understanding of AI application security and reliability practices, including authorization, credential protection, sensitive information handling, constrained tool access, recoverable workflows, and appropriate 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 communicate considerations and tradeoffs to both technical and non‑technical stakeholders.
• Demonstrated ability to mentor and train engineers, conduct effective code reviews, and contribute to technical standards and best practices while maintaining significant hands‑on engineering work.

Technologies

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

Preferred Skills and Knowledge

• Production experience using both n8n and LangGraph, especially solutions combining workflow automation with custom Python‑based agent services.
• Experience deploying agents via 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 Model Context Protocol (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 provide visibility into 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.

Compensation and Location

Location: Nashville, TN (onsite). Salary: USD 170,000 to 200,000 per year.

Physical Requirements

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