Lead AI/ML Engineer

AI/ML Lead

Location: Dallas TX/Tampa FL/Jersey City NJ (Hybrid 3 Days WFO) Job type: Contract

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 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.