Role - AI/ML Lead
Location - Dallas, TX I Tampa, FL I Jersy City, NJ ( 3 days WFO)
Interview Mode: Need to go inperson for Karat assessment (Local candidates only)
Engagement Type: Contract to Hire after 6 months
Job Description:
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 Google Cloud Platform). 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 Google Cloud Platform).
- 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, Google Cloud Platform).
- 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.