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 at scale.
• Diagnose 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 PyTorch.
• 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.
• Minimum 6–10+ years of relevant work experience in AI, machine learning, and applying data science to real‑world use cases.
• 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.

Experience our comprehensive benefits package, including family medical, vision, and dental coverage, a competitive base salary, and eligibility for equity awards and discretionary bonuses or commissions.