• Design, build, and maintain scalable, fault-tolerant AI systems leveraging OpenAI and Anthropic models. • Develop RAG architectures to ensure efficient, high-performance information retrieval tailored to chemical and materials science. • Optimize system performance to handle large-scale data and application demands. • Build and maintain intelligent AI agents using modern frameworks. • Collaborate with domain experts to refine agent capabilities for specific scientific workflows. • Architect and maintain vector database solutions for efficient data storage and retrieval. • Develop pipelines for ingestion, transformation, and storage to enable AI/ML workflows. • Implement robust error-handling, monitoring, and alerting mechanisms to ensure system resilience. • Troubleshoot and resolve system bottlenecks and failures. • Design, implement, and maintain CI/CD pipelines for AI systems and data workflows. • Stay informed on the latest trends and tools in AI/ML engineering and agent technologies. • Work closely with AI/ML, data engineering, and platform teams to understand and deliver on technical requirements.

• Design and implement end-to-end AI and LLM-based systems • Work extensively with Large Language Models (LLMs) • Build and maintain advanced Prompt Engineering frameworks • Design and manage Knowledge Bases for AI (company content, documents, data) • Fine-tune and train AI models using internal organizational data • Implement RAG architectures using Vector Databases • Integrate and orchestrate multiple AI APIs (OpenAI, Anthropic, Gemini, Azure AI, etc.) • Convert AI outputs into usable tools for sales, marketing, content, and analytics teams • Design AI infrastructure ready for SaaS and client-facing products • Collaborate closely with Full Stack Developers and business stakeholders

• Own architecture decisions and define the technical roadmap for AI systems that scale with organizational needs • Provide technical leadership and mentorship, promoting best practices in coding, architecture, and system design • Oversee the design and implementation of machine learning infrastructure — from data pipelines to model deployment, ensuring reliability and performance • Implement and uphold high engineering standards, including testing, CI/CD, and code review processes • Lead project planning and execution, managing priorities, timelines, and deliverables in a remote environment • Collaborate cross-functionally with product, data, and DevOps teams to align AI initiatives with business goals • Evaluate and integrate new AI/ML frameworks, tools, and technologies to drive innovation and optimize performance • Monitor system health and model performance, proactively addressing scalability, accuracy, and reliability • Foster a culture of continuous improvement, career growth, and knowledge sharing within the team.

• Lead a new B2B SaaS business built around AI and platform innovation in a vertical software market • Own AI strategy and execution: identify, prioritize, and deliver high-impact AI use cases • Build and ship production-ready AI solutions that drive real customer and revenue outcomes • Partner with Product, Sales, and Operators to ensure strong adoption and commercialization • Define and execute a scalable go-to-market strategy, from early adopters to first customers • Set product and market strategy, with a strong focus on developer experience, scalability, and integration • Build a milestone-based plan, allocate budget, and support fundraising with Valsoft • Assemble and lead a high-performing cross-functional team (product, tech, sales, operations) • Work closely with technical leaders to deliver high-quality APIs, SDKs, documentation, and integrations • Own and operate the business end-to-end, with full autonomy