Senior Generative AI Developer

Role - AI/ML Lead 

Fulltime 

H1 transfer is fine to submit 

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.