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Sr GenAI Engineer; In-person Dallas, TX

Job in Dallas, Dallas County, Texas, 75215, USA
Listing for: Saransh Inc
Part Time, Contract position
Listed on 2026-01-12
Job specializations:
  • Software Development
    AI Engineer, Software Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 100000 - 125000 USD Yearly USD 100000.00 125000.00 YEAR
Job Description & How to Apply Below
Position: Sr GenAI Engineer (In-person Interview is required in Dallas, TX)

Sr GenAI Engineer

Location: Dallas, TX (3 days a week onsite required, in-person interview mandatory).

Job Type: Contract.

Role

Summary:

We are looking for a highly experienced Senior Lang Graph Developer to architect and implement cutting-edge agentic workflows using Lang Graph. This role demands deep expertise in building stateful, multi-agent systems that leverage LLMs for complex decision-making, orchestration, and automation.

Key Responsibilities
  • Design and implement advanced Lang Graph workflows with complex node logic, branching, and memory management.
  • Lead the development of agentic systems that interact, reason, and adapt dynamically.
  • Optimize performance and scalability of Lang Graph graphs in production environments.
  • Integrate Lang Graph with external APIs, databases, and LLMs to create seamless, intelligent pipelines.
  • Mentor junior developers and establish best practices for Lang Graph development.
  • Collaborate with AI researchers and product teams to translate abstract ideas into robust Lang Graph implementations.
Required Skills & Experience
  • 8+ years in software engineering, with at least 3-4 years in Lang Graph or agentic frameworks.
  • Expert-level proficiency in Python, asynchronous programming, and graph-based computation.
  • Deep understanding of LLM orchestration, memory management stateful agent design.
  • Experience with Lang Chain, OpenAI, Anthropic, or similar LLM platforms.
  • Strong grasp of workflow debugging, graph visualization, and Lang Graph Studio.
  • Proven ability to build production-grade agentic systems with high reliability and fault tolerance.
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