Job
Description
About the Role: We are seeking a highly skilled and hands-on GenAI expert to join our team and help shape our AI strategy from the ground up. The ideal candidate will not only bring deep technical knowledge but also a product-first mindset and a passion for delivering value through responsible and efficient AI deployment. This is a high-impact role with the potential to lead and build a team around. Key Responsibilities: Develop and refine GenAI applications leveraging foundation models (LLMs, VLMs) for real-world use cases. Fine-tune foundation models using proprietary and domain-specific data to enhance model relevance and performance. Own the full AI lifecycle including experimentation, evaluation, production readiness, and value realization. Define and track key AI metrics; implement monitoring and feedback loops to measure model effectiveness post-deployment. Apply traditional ML techniques (clustering, classification, vector search) as complementary strategies where appropriate. Build and deploy AI agent frameworks that can autonomously interact with tools, data stores, and other models to solve tasks end-to-end. Collaborate cross-functionally to integrate GenAI systems into existing platforms, ensuring scalability, efficiency, and business alignment. Ask the right questions to iterate, refine, and evolve AI solutions. Qualifications: Proven experience building GenAI-powered applications using LLMs, VLMs, and custom pipelines. Strong knowledge of model fine-tuning techniques and prompt engineering using proprietary data. Practical understanding of AI productization, lifecycle, metrics, and monitoring strategies. Hands-on experience with AI agent frameworks and related orchestration tools. Ability to articulate technical solutions, integration patterns, and tradeoffs effectively. Experience with Python, ML libraries (e.g., Hugging Face, LangChain, PyTorch, TensorFlow), and deployment in cloud environments (AWS, Azure, GCP). Preferred: Experience in a startup or innovation lab environment. Familiarity with vector databases (e.g., Pinecone, FAISS) and retrieval-augmented generation (RAG). Exposure to ethical AI, model interpretability, and responsible deployment practices. Show more Show less
