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About the Role
The AI Engineer is responsible for designing, developing, testing, and deploying AI-enabled solutions to support the Firm’s legal and business operations. This role translates business needs into practical, scalable AI solutions using various technologies and collaborates with various teams throughout the development lifecycle.
Responsibilities
- Partner with attorneys, practice groups, and business teams to identify AI opportunities, understand workflows, define requirements, and establish measurable success criteria.
- Design, develop, test, and deploy AI agents and multi-step workflows using platforms and frameworks such as Azure Databricks, n8n, LangGraph, and Databricks Agent Bricks.
- Integrate AI solutions with enterprise applications, APIs, governed data sources, and native Microsoft Azure services, incorporating appropriate human review and approval processes.
- Develop prototypes in collaboration with users and translate successful concepts into reliable, scalable production 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 continuously improve AI solutions.
- Collaborate with Data Architecture and DevOps teams to support governed data access, application deployment, environment configuration, monitoring, and production support.
- Design AI solutions with appropriate reliability safeguards, including error handling, retries, timeouts, stopping conditions, recovery processes, and controls designed to prevent unintended or duplicate actions.
- Apply security, access controls, data protections, source traceability, and responsible AI practices throughout solution design and deployment, including protections against prompt injection, inappropriate access, and data leakage.
- Engage with users following deployment to gather feedback, support adoption, measure business impact, and identify opportunities for continued enhancement.
- Participate in code reviews, technical documentation, knowledge sharing, and the development of reusable components, standards, and engineering best practices.
Requirements
- Strong Python programming skills and demonstrated ability to develop 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 the relationship between source quality and AI system performance.
- Experience evaluating and troubleshooting AI applications, including analyzing execution traces, failures, task completion, quality, latency, or cost.
- Understanding of production engineering practices, including application deployment, configuration management, secrets management, monitoring, automated testing, and troubleshooting.
- Working knowledge of authentication, authorization, role-based access controls, and security considerations associated with enterprise AI applications and sensitive information.
- Strong analytical and problem-solving skills with the ability to translate ambiguous business requirements into practical technical solutions.
- Ability to communicate technical concepts and tradeoffs effectively to both technical and non-technical stakeholders.
- Demonstrated collaboration, accountability, curiosity, and ability to work effectively with engineers, domain experts, and business users.
Skills
- Azure Databricks
- Microsoft Azure
- n8n
- LangGraph
- Databricks Agent Bricks
- Python
- SQL
- APIs
- Automated testing
- Version control
- Code review
- Retrieval-augmented generation (RAG)
- Embeddings
- Prompt design
- Context design
- Structured outputs
- Tool integration
- State management
- Error handling
- Human-in-the-loop workflows
- Production engineering
- Application deployment
- Configuration management
- Secrets management
- Monitoring
- Troubleshooting
- Authentication
- Authorization
- Role-based access controls
- Security
- TypeScript
- JavaScript
- React
- MLflow
- LangSmith
- Spark
- Delta Lake
- Unity Catalog
- Lakebase
- Delta Sharing
- Vector search
- Hybrid retrieval
- Filtering
- Reranking
- Document processing
- Metadata extraction
- Graph databases
- Knowledge graphs
- Graph-based RAG
- PostgreSQL
- Streaming responses
- Citations
- Progress indicators
- Feedback mechanisms
- Model Context Protocol (MCP)
- Benchmarks
- Regression testing
- Automated scoring
- LLM-based evaluation methods
- Statistical analysis
- Experimental design
- Microsoft Entra ID
- Managed identities
Location
- Nashville, Tennessee
Work Type
- Onsite
Experience Level
- Mid-level
- Senior
Salary/Compensations
- $85,000 - $170,000
About the Company
- Pillsbury Winthrop Shaw Pittman LLP is an Equal Opportunity Employer.
Equal Opportunity
- If you require an accommodation in order to apply for a position, please contact us at PillsburyWorkday@pillsburylaw.com.