Senior AWS Serverless Engineer

Role : Gen AI Architect Location : Pleasanton , California We are seeking an experienced Generative AI Architect to lead the design, development, and deployment of cutting-edge generative AI systems. The ideal candidate will combine deep technical knowledge of AI / ML (particularly large language models and diffusion models) with strong architecture and leadership skills. You will play a critical role in shaping our AI strategy and enabling innovative products powered by generative technologies Key Responsibilities : Architect and design end-to-end generative AI solutions (text, image, audio, or multimodal) that align with business objectives. Evaluate and select appropriate foundation models (e.g., GPT, LLaMA, Stable Diffusion) and fine-tuning strategies. Lead the development of custom LLM applications , including prompt engineering, fine-tuning, RLHF, and model compression. Collaborate with cross-functional teams (engineering, product, design, data science) to integrate AI into products and platforms. Ensure responsible and ethical AI practices are embedded in system design (e.g., fairness, privacy, explainability). Guide the implementation of AI infrastructure (data pipelines, vector databases, model serving, APIs). Stay up-to-date on the latest AI research and tools, and make recommendations for adoption. Conduct proofs-of-concept , prototypes, and performance benchmarking. Mentor junior engineers and contribute to best practices and internal knowledge sharing. Required Qualifications : Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Machine Learning 7+ years of experience in AI / ML, with 3+ years in generative AI (LLMs, diffusion models, etc.). Proven experience designing and deploying large-scale AI systems. Deep understanding of transformer architectures , tokenization , and pretraining / fine-tuning paradigms . Hands-on experience with AI / ML frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, etc. Strong knowledge of MLOps, cloud platforms (AWS, Google Cloud Platform, Azure), and scalable architectures (e.g., microservices, serverless). Experience with vector databases (e.g., Pinecone, Weaviate, FAISS) and retrieval-augmented generation (RAG) systems. Familiarity with responsible AI frameworks and privacy-preserving techniques.