Gen AI Developer

Job Title : Gen AI Developer

Location : Alpharetta, GA / NY / NJ (Day 1 Onsite)

Job Summary

Value Technology is seeking a highly skilled Generative AI Developer to design, build, and deploy AI-driven solutions using LLMs, generative models, and modern AI frameworks. The ideal candidate will have experience in building conversational agents, retrieval-augmented generation (RAG) systems, fine-tuning large language models, and integrating AI capabilities into enterprise applications.

Preferred Qualifications

• 10+ years experience in AI / ML development.
• 5-7 years hands-on experience with LLMs or Gen AI.
• Bachelor s or Master s degree in Computer Science, AI / ML, Data Science, or related fields.
• Develop, fine-tune, and deploy Large Language Models (LLMs) for various use cases such as chatbots, document automation, code generation, and summarization.
• Build RAG pipelines using vector databases, embeddings, and retrieval frameworks.
• Implement prompt engineering and prompt-chaining for optimal model performance.
• Experience building production-grade AI systems.
• Experience with agent frameworks (LangGraph, OpenAI Assistants API).
• Experience in multimodal AI (image, speech, video) is a plus.
• Understanding of security, compliance, and governance for AI models.
• Experience with Python, Hugging Face, LangChain.
• Experience designing and deploying RAG / LLM workflows.
• Strong understanding of embedding models and vector search.
• Experience developing enterprise-grade Gen AI products.
• Work closely with product managers, data scientists, ML engineers, and software developers.
• Design end-to-end Gen AI system architecture including data pipelines, vector search, orchestration, and model optimization.
• Integrate generative AI models with enterprise systems through APIs, microservices, and cloud services.
• Document models, pipelines, and solution architecture.
• Containerize and deploy models using Docker, Kubernetes, or serverless frameworks.
• Implement CI / CD pipelines for AI workflows.
• Monitor and improve AI model performance post-deployment.
• Prepare and manage datasets for LLM training, embeddings, and retrieval.
• Build pipelines for data ingestion, cleaning, transformation, and annotation.
• Work with structured and unstructured data, including documents, PDFs, chat logs, code, and multimedia.