Senior AI Engineer: Agent Harness & LLM Platforms

Relativity

Wrocław

Hybrid

PLN 245,000 - 367,000

Full time

13 days ago
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Benefits offered by this job

DTO
Parental leave
Equity program

Job summary

Relativity is seeking a Senior AI Engineer to join the Agent Harness team. You will help shape the architecture of Relativity’s agent runtime, adopting LangGraph, LangChain, and related frameworks to empower agent-powered workflows at scale.

The role emphasizes extending capabilities like long-session handling and cross-product integration with aiR Assist and ClaiR, while maintaining robust runtime abstractions.

Qualifications

  • 5+ years of professional software engineering experience with strong Python
  • Experience designing LLM-powered or agentic systems using LangChain, LangGraph, or equivalent
  • Experience with tool calling, orchestration, and agent state management
  • Solution architecture with reusable abstractions and foundational building blocks
  • Solid understanding of asynchronous Python, API design, and PostgreSQL

Responsibilities

  • Shape the architecture of Relativity’s agent runtime and runtime patterns
  • Mature the agent harness with long-session handling and broader protocol support
  • Define first-class platform capabilities and clean abstractions for downstream teams
  • Operate in a rapidly evolving AI ecosystem with focus on scalable, reliable systems

Skills

Python
LangChain
LangGraph
Orchestration
Agent state management
PostgreSQL
Azure
CI/CD
Docker
Kubernetes
English proficiency

Education

Bachelor’s degree in Computer Science or Engineering

Tools

Docker
Kubernetes
PostgreSQL

Job description

Relativity is seeking a Senior AI Engineer to join the Agent Harness team. You will help shape the architecture of Relativity’s agent runtime, adopting LangGraph, LangChain, and related frameworks to empower agent-powered workflows at scale.

The role emphasizes extending capabilities like long-session handling and cross-product integration with aiR Assist and ClaiR, while maintaining robust runtime abstractions.

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