Gen AI Developer Wells Fargo

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.