Lead Machine Learning Engineer - Agentic Models, LLM, RAG, GenAI

Responsibilities

Lead the team in: research, design, development, and deployment of advanced AI agents and agentic systems. Architect and implement complex multi‑agent systems, including planning, decision‑making, and execution capabilities. Develop and integrate large language models (LLMs) and other state‑of‑the‑art AI techniques to enhance agent autonomy and intelligence. Build robust, scalable, and reliable infrastructure to support the deployment and operation of AI agents gnose and troubleshoot issues in complex distributed environments and optimize system performance.

Contribute to the team's technical growth and knowledge sharing. Stay up‑to‑date with the latest advancements in AI research and agentic AI and apply them to our products. Leverage enterprise data, market data, and user interactions to build intelligent and personalized agent experiences.

• Knowledge and passion in machine learning algorithms, Gen AI, LLMs, and natural language processing (NLP)
• Understanding of agent-based modeling, reinforcement learning, and autonomous systems
• Ability to innovate, as proven by a track record of software artifacts or academic publications in applied machine learning
• Experience with large language models (LLMs) and their applications in Agentic AI
• Proficiency in programming languages such as Python and experience with machine learning frameworks like Tensor Flow or Py Torch
• Experience with cloud platforms (AWS) and containerization technologies (Docker, Kubernetes)
• Understanding of distributed system design patterns and microservices architecture
• Excellent problem‑solving and data analysis skills
• Strong communication and collaboration skills
• Master's or Ph.D. in Computer Science, Artificial Intelligence, or a related field, or equivalent years of experience
• Min 5‑7+ years of relevant work experience in AI, Machine Learning, and applying data science to real‑world use cases OR Strong track record of taking systems from prototype to production with a focus on scalability and reliability
• Knowledge of fine‑tuning strategies (QLORA, DPO) and inference optimization (vLLM, Tensor
  
  RT‑LLM)
• Research experience in agentic AI or related fields
• Experience building and deploying AI agents in real‑world applications

Family medical, vision and dental coverage, a competitive base salary, and eligibility for equity awards and discretionary bonuses or commissions.