Artificial Intelligence (AI), Leading Edge Technology, Python Programming/Scripting Language
Location:
Dallas TX/ Tampa FL/ New Jersey - Hybrid
Fulltime/FTE
Salary: Market
Client: Bank
Role Overview
We are seeking an experienced
Senior Generative AI Developer to design and implement cutting-edge AI solutions leveraging
Retrieval-Augmented Generation (RAG) techniques. The ideal candidate will have strong expertise in
Python programming ,
FastAPI , and cloud platforms (AWS, Azure, or GCP). This role requires a deep understanding of
system architecture design , scalable APIs, and end-to-end AI solution development.
Key Responsibilities - Architect and develop Generative AI applications using RAG frameworks for enterprise-scale solutions.
- Design and implement robust system architectures for AI-driven platforms ensuring scalability, security, and performance.
- Build and optimize APIs using FastAPI for seamless integration with AI models and data pipelines.
- Collaborate with cross-functional teams to integrate AI solutions into existing systems and workflows.
- Implement data ingestion, preprocessing, and retrieval mechanisms for large-scale knowledge bases.
- Ensure compliance with best practices for cloud deployment (AWS, Azure, or GCP).
- Conduct performance tuning and optimization of AI models and APIs.
- Stay updated with the latest advancements in Generative AI, LLMs, and RAG methodologies .
Required Skills & Qualifications - 8+ years of professional experience in software development and system design.
- Strong proficiency in Python and experience with FastAPI for API development.
- Hands-on experience with Generative AI frameworks and RAG architectures .
- Solid understanding of system and architecture design principles for distributed applications.
- Experience deploying solutions on any major cloud platform (AWS, Azure, GCP).
- Familiarity with vector databases , embedding models , and retrieval pipelines .
- Strong problem-solving skills and ability to work in a fast-paced environment.
Preferred Qualifications - Experience with LLM fine-tuning , prompt engineering , and model evaluation .
- Knowledge of containerization (Docker) and orchestration (Kubernetes) .
- Exposure to CI/CD pipelines and DevOps practices .