AI Engineer

Design, build, and deploy AI-enabled solutions that translate legal and business workflows into reliable, scalable applications.

Responsibilities

• Partner with attorneys, practice groups, and business teams to identify AI opportunities, understand workflows, define requirements, and set measurable success criteria.
• Design, develop, test, and deploy AI agents and multi-step workflows using Azure Databricks, n8n, LangGraph, and Databricks Agent Bricks.
• Integrate AI solutions with enterprise applications, APIs, governed data sources, and native Microsoft Azure services, including human review and approval processes.
• Develop user-collaborative prototypes, then transform successful concepts into production-ready, scalable applications.
• Build and maintain evaluation datasets, automated tests, and quality measures to assess agent behavior, retrieval quality, tool-use accuracy, task completion, performance, latency, and cost.
• Analyze execution traces, system behavior, and failure patterns to troubleshoot issues and improve AI solution reliability.
• Collaborate with Data Architecture and DevOps teams for governed data access, application deployment, environment configuration, monitoring, and production support.
• Implement reliability safeguards including error handling, retries, timeouts, stopping conditions, recovery processes, and controls to prevent unintended or duplicate actions.
• Apply security, access controls, data protections, source traceability, and responsible AI practices, including protections against prompt injection, inappropriate access, and data leakage.
• After deployment, gather user feedback, support adoption, measure business impact, and identify opportunities for continued enhancement.
• Participate in code reviews, create and maintain technical documentation, share knowledge, and develop reusable components, standards, and engineering best practices.

Requirements

• Python proficiency with experience building maintainable, production-quality software using APIs, automated testing, version control, and code review.
• Working knowledge of SQL and experience integrating application, data, and AI components.
• Hands-on experience developing AI agents, agent-enabled applications, or multi-step AI workflows involving language models, enterprise information, APIs, or external tools.
• Experience with prompt and context design, structured outputs, tool integration, state management, error handling, and human-in-the-loop workflows.
• Practical experience with Azure Databricks and working knowledge of Microsoft Azure services used for application development, integration, or deployment.
• Understanding of retrieval-augmented generation (RAG), embeddings, retrieval techniques, and how source quality impacts AI performance.
• Experience evaluating and troubleshooting AI applications, including analysis of execution traces, failures, task completion, quality, latency, and cost.
• Understanding of production engineering practices: deployment, configuration management, secrets management, monitoring, automated testing, and troubleshooting.
• Working knowledge of authentication, authorization, role-based access controls, and security considerations for enterprise AI applications handling sensitive information.
• Strong analytical and problem-solving skills to convert ambiguous business needs into practical technical solutions.
• Ability to communicate technical concepts and tradeoffs to both technical and non-technical stakeholders.
• Collaboration and accountability with curiosity, and ability to work effectively with engineers, domain experts, and business users.

Technologies

• Python, SQL
• Azure Databricks, Microsoft Azure
• n8n, LangGraph, Databricks Agent Bricks, LangSmith
• MLflow, Spark, Delta Lake, Unity Catalog, Lakebase, Delta Sharing
• Model Context Protocol (MCP)
• PostgreSQL, TypeScript, JavaScript, React
• Microsoft Entra ID

Preferred Skills & Knowledge

• Experience deploying, monitoring, and supporting AI applications in production.
• Experience with one or more orchestration/workflow technologies including n8n, LangGraph, Databricks Agent Bricks, or similar platforms.
• Experience with MLflow, LangSmith, or comparable AI tracing, observability, and evaluation tools.
• Advanced experience with Azure Databricks technologies such as Spark, Delta Lake, Unity Catalog, Lakebase, or Delta Sharing.
• Experience designing and optimizing RAG solutions, including vector search, hybrid retrieval, filtering, reranking, document processing, metadata extraction, and retrieval evaluation.
• Experience with graph databases, knowledge graphs, graph-based RAG, or techniques for identifying and linking entities and relationships across information sources.
• Experience with PostgreSQL or similar technologies for persistent agent state, checkpoints, or memory.
• Front-end or application development experience with TypeScript, JavaScript, React, or similar technologies.
• Experience designing user-facing AI applications featuring streaming responses, citations, progress indicators, feedback mechanisms, and human review.
• Experience developing reusable AI tools and enterprise integrations, including Model Context Protocol (MCP) or comparable approaches.
• Experience with evaluation datasets, benchmarks, regression testing, automated scoring, or LLM-based evaluation methods.
• Knowledge of statistical analysis or experimental design for evaluating AI system performance and improvements.
• Experience with Microsoft Entra ID, managed identities, and enterprise authentication/authorization models.
• Experience working in legal services, professional services, technical consulting, or other environments with sensitive information and complex business workflows.

Location

• Nashville, TN (onsite)

Salary

• USD 85,000 - 170,000 per year

Physical Requirements

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