• Own the Problem Space: Translate ambiguous product concepts into concrete ML strategies • Architect for Healthcare: Fine-tune multi-modal generative models and orchestrate sophisticated AI agents • Establish Rigorous Evals: Define strict statistical and business metrics from day one • Ship & Monitor: Write production-grade Python and collaborate with engineering to deploy scalable solutions • Communicate & Execute: Operate with deep focus and autonomy

• Ensure that the contractor team delivers high-quality ML Scoring solutions (e.g., click and conversion predictions) for Sponsored Offers on schedule, maintaining responsibility for the team's technical output and project milestones. • Manage the training scope for deep learning models, including feature space definition and model lifecycle standards. • Orchestrate the transition of scoring responsibilities from existing teams in cooperation with backend and product stakeholders. • Provide expert insight to future roadmaps for the ML scope of Sponsored Offers.

• Own end-to-end delivery of agentic, full-stack systems from first prototype to stable production, embedded alongside defense and intelligence customers. • Build and deploy ML services leveraging LLMs, embeddings, RAG, and agent orchestration into production environments, including classified and air-gapped ones. • Work directly with customers to scope problems, sequence delivery, and ship novel AI applications under real-world constraints. • Codify repeatable patterns into reusable tools and building blocks that help the team ship faster.

• Serve as a force multiplier for development teams by creating golden paths that remove roadblocks and improve ideation and innovation. • Collaborate with other engineers, product managers, and internal stakeholders in an Agile environment. • Provide mentorship, technical guidance, and perform code reviews for team members. • Design and deliver on projects end-to-end with little to no guidance. • Provide support to teams building and deploying AI applications by addressing common pain points in the MLDLC. • Learn constantly and be passionate about discovering new tools, technologies, libraries, and frameworks (commercial and open source), that can be leveraged to improve PitchBook’s AI capabilities. • Support the vision and values of the company through role modeling and encouraging desired behaviors. • Participate in various cross-functional company initiatives and projects as requested. • Contribute to strategic planning in a way that ensures the team is building exceptional products that bring real business value. • Evaluate frameworks, vendors, and tools that can be used to optimize processes and costs with minimal guidance.