Generative AI Engineer

Position Summary

The Senior Generative AI Developer will be a key contributor responsible for the design, development, testing, and deployment of production-grade Generative AI applications. This hands-on role requires deep expertise in modern software development practices, proficiency in Large Language Models (LLMs), and the ability to integrate sophisticated AI capabilities into existing enterprise software systems. The ideal candidate will mentor junior team members and drive technical excellence in GenAI implementation.

Key Responsibilities and Duties

Generative AI Development and Implementation

• Code Development : Write clean, efficient, and well-documented code primarily in Python to build Generative AI features, microservices, and APIs.
• Prompt Engineering : Design, implement, and rigorously test advanced Prompt Engineering techniques (e.g., Chain-of-Thought, few-shot learning) to maximize model performance and adherence to task specifications.
• RAG System Development : Architect and implement high-performance Retrieval-Augmented Generation (RAG) pipelines, including data chunking, embedding generation, vector database interaction (e.g., Pinecone, ChromaDB), and context retrieval optimization.
• Model Integration : Integrate, customize, and deploy both commercial (OpenAI, Anthropic) and open-source (Llama, Mistral) LLMs via APIs, local inference, or cloud-based services (e.g., AWS Bedrock, Azure OpenAI).
• Model Fine-Tuning (LoRA) : Execute model fine-tuning and adaptation techniques, such as LoRA (Low-Rank Adaptation) and QLoRA, on domain-specific datasets to improve accuracy and reduce model size.

Collaboration and Mentorship

Required Qualifications and Experience

Technical Expertise

Professional Attributes