Generative AI Architect

Overview

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.

• 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.

• 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, Tensor Flow, Hugging Face Transformers, Lang Chain, etc.
• Strong knowledge of MLOps, cloud platforms (AWS, GCP, 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.

• Experience with open-source LLMs and model distillation/quantization techniques.
• Exposure to multimodal AI models (e.g., CLIP, DALL
  · E, Imagen).
• Contributions to AI/ML research (e.g., published papers, open-source projects).
• Experience building GenAI copilots, chatbots
  , or productivity tools.

• Strong problem-solving and analytical skills.
• Excellent communication and stakeholder management abilities.
• Ability to translate complex AI concepts into business value.
• Entrepreneurial mindset and passion for innovation.