Generative AI Engineer

We’re looking for a
Generative AI Engineer
to join our innovation pod, focused on designing and integrating
agentic AI systems
built on the latest
LLM and GenAI technologies
. You’ll collaborate with technical leads and full-stack engineers to bring intelligent, production-grade AI capabilities to enterprise applications.

Vetting Process

1. Technical Interview
2. 15-Minute Delivery Connect
3. **Onsite (Face-to-Face) Interview – Richardson, TX (1 Hour, Client Round)

Key Responsibilities**

• Design, develop, and deploy agentic AI systems using state-of-the-art LLM frameworks.
• Integrate Generative AI models into full-stack applications and internal business workflows.
• Collaborate on prompt engineering , model fine-tuning , and output evaluation .
• Build reusable AI components for multi-agent orchestration and task automation.
• Optimize AI inference pipelines for scalability, latency, and cost efficiency.
• Contribute to technical architecture and the long-term AI roadmap.

**Core Skills & Experience

Must Have**

• 4–8 years of software engineering experience (with at least 1–2 years in AI/ML or GenAI).
• Strong Python development background (for AI/ML model integration).
• Hands-on experience with LLM frameworks – LangChain and LlamaIndex (Required).
• Practical exposure to agentic frameworks – LangGraph , AutoGen , or CrewAI (Required).
• Working knowledge of Git , CI/CD , DevOps , and production-grade GenAI deployment .

Nice to Have

• Experience with Google Cloud Platform (GCP) – especially Vertex AI , Cloud Run , or GKE .
• Familiarity with AI APIs , embeddings , vector databases , and search integrations .
• Experience with fine-tuning open-source models (LLaMA, Mistral, etc.) or OpenAI API integration .
• Exposure to multi-modal AI systems (text, image, or voice).
• Experience with Low-Code/No-Code tools (e.g., AppSheet ) for workflow automation.

Preferred Academic Background

Candidates from
strong academic institutions
with a background in
Computer Science, AI/ML, or related fields
are preferred.