Location: Richardson, TX (Onsite) Duration: Longterm Contract Interview Mode: Face-to-Face Local candidate only
As a Generative AI Engineer, you’ll be a core member of this pod, building and integrating agentic systems powered by cutting-edge LLM and GenAI technologies. You’ll work closely with Tech Leads and Full Stack Engineers to turn AI capabilities into production-ready enterprise solutions.
Key Responsibilities
Design, develop, and deploy agentic AI systems leveraging LLMs and modern AI frameworks.
Integrate GenAI models into full-stack applications and internal workflows.
Collaborate on prompt engineering, model fine-tuning, and evaluation of generative outputs.
Build reusable components and services for multi-agent orchestration and task automation.
Optimize AI inference pipelines for scalability, latency, and cost efficiency.
Participate in architectural discussions, contributing to the pod’s technical roadmap.
Core Skills & Experience Must Haves
4–8 years of software engineering experience with at least 1–2 years in AI/ML or GenAI systems in production
Hands-on experience with Python only for AI/ML model integration.
Experience with LLM frameworks (LangChain, LlamaIndex is a must)
Exposure to agentic frameworks (Langgraph, AutoGen, CrewAI is a must)
Understanding of Git, CI/CD, DevOps, and production-grade GenAI deployment practices.
Familiarity with Google Cloud Platform (GCP) — e.g. Vertex AI, Cloud Run, and GKE.
Good-to-Have
Experience building AI APIs, embeddings, vector search, and integrating them into applications.