Senior AI Platform Engineer (LLM/SLM)

We are seeking a dynamic and innovative individual to join our remote team. We offer a competitive salary of up to $200,000 annually, along with a collaborative and supportive work culture. Our team thrives on creativity and open communication, encouraging all members to bring their unique ideas to the forefront as we navigate complex industry needs together. - Builder mentality - Pragmatic approach to problem-solving - Comfortable navigating uncertainty - Strong collaborative and communication skills - Ability to simplify complex concepts - Translate industry-specific needs (insurance, credit unions, healthcare) into LLM-driven workflows - Insight into business user perspectives, beyond just technical understanding - Proficient in developing LLM/SLM pipelines - Experience integrating with NEMESIS backend systems - Skilled in creating APIs, agents, and orchestration frameworks - Familiarity with predictive modeling principles - Ability to integrate LLMs with structured analytics - Commitment to ensuring outputs are reliable, explainable, and safe - Understanding of how non-technical users engage with analytics - Capability to convert business processes into conversational interfaces - Ability to prioritize features that enhance user adoption - Knowledge of risk assessment, forecasting, fraud detection, and churn analysis - Experience in developing domain-aware prompts, agents, and workflows - Build and maintain LLM/SLM pipelines - Integrate solutions with our NEMESIS backend - Create and manage APIs, agents, and orchestration layers - Ensure a solid understanding of predictive modeling concepts - Wrap LLMs around structured data analytics for comprehensive insights - Make certain that outputs are reliable, safe, and easily explained - Understand and enhance how non-technical users interact with analytics - Translate business workflows into effective conversational interfaces - Focus on prioritizing features that increase user adoption - Develop prompts, agents, and workflows that are aware of the specific domain context