• Work with product, engineering, and teams to cultivate a shared vision for the problem space, constraints, priorities and ideal end state, and be able to articulate and advocate for this perspective. • Lead the full lifecycle of ML and LLM platform workstreams - from data ingestion, training, and deployment to the ongoing optimization of live user workflows based on performance metrics and feedback. • Work with internal teams and end users to develop a deep understanding of requirements, perform thoughtful solution design, use data to test hypotheses, and support teams throughout execution. • Write detailed user stories for new features, capturing detailed descriptions of business rationale, requirements, and success criteria that are defined by measurable outcomes. • Develop short and long term roadmaps that deliver maximum value with minimum risk and assume ongoing iteration. • Partner with AI Researchers to mature proofs of concept into production ready capabilities. Perform hands-on exploration of user feedback, healthcare records and validation data to validate model performance.

• Lead the Artificial Intelligence team and serve as the technical reference • Define architecture and technical standards for the team’s AI/ML solutions • Conduct code reviews and ensure delivery quality • Mentor and develop the team’s engineers technically • Support technology, tooling, and approach decisions in collaboration with management • Architect and implement multi-agent systems using AWS Bedrock and LLMs • Define prompt engineering standards and language model evaluation practices • Lead the end-to-end implementation of generative AI solutions • Guide the development of the team’s predictive models and data pipelines • Ensure best practices for experimentation, versioning, and documentation • Translate business needs into viable, scalable ML solutions • Establish and evolve CI/CD and MLOps practices within the team • Define monitoring strategies for models in production • Collaborate with other squads to integrate AI solutions sustainably

• Drive measurable business impact across H.I.G. Capital’s portfolio companies through AI-powered automation. • Evaluate and select third-party AI platforms and vertical SaaS tools. • Lead end-to-end implementation; configure workflows, integrate with existing enterprise systems. • Define baselines and success metrics before every deployment. • Train portfolio company teams on deployed solutions to enable independent operation.

• Develop modern web widgets with Angular for our frontend • Design and implement APIs based on OpenAPI • Optimize our Market Engine backend built in Java • Implement automated end-to-end tests • Define infrastructure as code using AWS and CDK • Work according to DevOps principles • Build and operate serverless applications within AWS • Support new product directions in the ImmoConnect environment