Advanced AI Engineer
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Job Description
Advanced AI Engineer building Relativity’s Python agent runtime for legal data intelligence. Extending LangGraph-based orchestration, memory, protocols, and production AI capabilities.
Responsibilities:
- Build and extend Relativity’s agent runtime in Python using LangGraph, LangChain, and Deep Agents
- Implement stateful graph agents, harness profiles, and multi-agent/subagent orchestration
- Develop streaming and structured/generative output, tool calling, human-in-the-loop review, conversation branching, message queues, session memory, and checkpoint/resume capabilities
- Contribute to the harness extensibility and protocol layer, including MCP tool servers and clients, A2A interoperability, and runtime registries
- Enable agents to use appropriate models across LLM providers such as OpenAI and Gemini
- Write clean, well-tested, production-ready code
- Participate in design and code reviews
- Own components through production delivery
- Collaborate with teammates, Applied Science, and aiR application teams to move capabilities from experimentation into production safely
Requirements:
- 3+ years of professional software engineering experience with strong, recent Python experience building production systems
- Experience building LLM-powered or agentic systems with LangChain, LangGraph, or equivalent frameworks, including tool calling, orchestration, and agent state management
- Good design instincts, including clean, testable code and component design
- Solid understanding of modern async Python, API and service design, and relational data, especially PostgreSQL
- Experience delivering cloud-native systems with Azure or similar, CI/CD, and Docker; familiarity with Kubernetes
- Bachelor’s degree in Computer Science, Engineering, or equivalent experience
- English proficiency for technical communication
- Preferred: experience with Deep Agents, multi-agent/subagent architectures, agent memory, or human-in-the-loop patterns
- Preferred: familiarity with Model Context Protocol (MCP) or agent-to-agent (A2A) interoperability
- Preferred: experience with RAG and retrieval systems, citations, and evaluating LLM output quality
- Preferred: familiarity with LLM observability and tracing, including MLflow and OpenTelemetry, and evals for safe model upgrades
- Preferred: infrastructure-as-code experience with Pulumi or Terraform
Benefits:
- Competitive salary
- Benefits
- DTO
- Parental leave
- Equity program
- Annual performance bonus
- Long-term incentives
- Growth and ownership opportunities
- Knowledge sharing and continuous improvement
- Inclusive, diverse work environment
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