Senior AI Lead, Qantev

• S. or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or related field
• 6+ years of experience in AI/ML development, with a strong track record in deep learning
• Expertise in building and deploying models using PyTorch, TensorFlow, or JAX
• Experience with transformer architectures, diffusion models (e,g. Gemini Diffusion) and reinforcement learning
• Proven experience with agent frameworks (e.g., LangGraph SmolAgent, Swarn)
• Experience with LLMOps - (e.g. Langsmith, Langfuse)
• Strong understanding of knowledge graphs, planning systems, memory modules, and retrieval-augmented generation (RAG)
• Experience leading AI research or applied ML teams
• Strong software engineering skills, including experience with large-scale ML pipelines
• Be Pro-active, Product minded and Business driven
• Strong leadership and team-building skills with a proven ability to inspire, motivate, and develop data scientists & ML engineers
• Excellent problem-solving, analytical, and troubleshooting skills
• Strong written and verbal communication skills with the ability to explain complex technical concepts to non-technical stakeholders
• Ability to work in a fast-paced, dynamic environment, managing multiple priorities and deadlines

Desirable

• We are seeking a Senior AI Lead with deep expertise in Deep Learning, Foundation Models, and Agentic AI systems to drive our next generation of intelligent applications
• You will lead a high-performing team of data scientists and ML engineers in developing AI-driven SaaS Product for Healthcare industry
• This role requires a balance of hands-on technical leadership, architectural oversight, and strategic vision to translate cutting-edge AI research into production-level solutions that deliver business value
• Technical Leadership: Lead the design and development of deep learning models and agent-based AI architectures for Qantev’s Healthcare Claims Data Platform
• Research & Development: Stay at the forefront of AI advancements in areas such as transformer-based models, reinforcement learning, multi-agent systems, and LLM-based tool use
• Agentic AI Development: Architect and deploy AI agents with capabilities including long-term memory, planning, reasoning, and autonomous tool use
• Team Management: Mentor a team of AI scientists and ML engineers, fostering an environment of innovation and collaboration
• Product Integration: Work cross-functionally with product, engineering, and UX teams to bring AI solutions from prototype to scalable product
• Evaluation & Metrics: Establish benchmarks, A/B tests, and interpretability metrics for model performance and agent behavior
• Ethics & Safety: Champion responsible AI practices, including model transparency, fairness, and compliance with Privacy & Security policies & practices