AI Engineer

• Design and improve Paxos’s AI workflows. Own the prompt chains, agent patterns, structured-output schemas, evals, and test cases that power customer implementations.
• Turn reimbursement complexity into testable workflow logic. Translate payer rules, document requirements, clinical evidence standards, and operational edge cases into clear AI system behavior.
• Own AI quality and reliability. Analyze failures, improve prompts and schemas, and build evals that catch issues before they reach production.
• Work directly with customers during implementation. Clarify requirements, understand edge cases, and translate what we learn into workflows and reusable Paxos capabilities.
• Document how the system works. Write workflow specs, test plans, prompt/eval documentation, and implementation notes that help the team build and improve faster.

Founding AI Lead

• Design and improve Paxos’s AI workflows.
• Turn reimbursement complexity into testable workflow logic.
• Own AI quality and reliability.
• Work directly with customers during implementation.
• Document how the system works.

Founding Applied AI Lead

• Take part in building an AI search platform from scratch, and owning it end to end in a production environment.
• Design and implement full-stack solutions, including backend services, frontend interfaces, and database management.
• Work with cloud-based systems, containers, and modern software infrastructure to support performance, scale, and reliability.
• Collaborate closely with algorithm developers and internal users to expose complex capabilities in a simple, usable way.

Senior Full Stack Developer – AI Platform

• Own the full software development lifecycle (SDLC) for the SEA platform
• Lead hands-on design and code review across microservices, APIs, and AI-integrated application layers
• Drive the architecture of cloud-native applications on Azure
• Architect and oversee the engineering of LLM-powered applications
• Build robust MLOps practices
• Implement responsible AI engineering guardrails
• Define and own the application development standards across the engineering organization
• Build, lead, and grow a world-class engineering organization distributed across multiple geographies
• Define the multi-year engineering roadmap for SEA and Digital Workplace platforms
• Serve as the primary technical voice in executive and client conversations

Senior Director, Global Head of Solution Development – AI Platforms

• Develop and deploy agentic AI applications for clinical data interaction
• Build systems to extract patient phenotypes from clinical trial databases
• Construct AI and ML systems for data management and analysis
• Evaluate and monitor model performance in production
• Build pipelines for clinical trial databases

Applied AI Engineer, Clinical Informatics

• Own the product roadmap covering agentic execution environments, skills and MCP ecosystem, agent orchestration, and AI-powered code review
• Drive developer adoption through training, newsletters, cross-team projects, and feedback sessions
• Define and track success metrics
• Coordinate across teams as the product voice spanning various areas of the Developer Experience data platform
• Shape vendor strategy: evaluate external AI tooling
• Own ADAS ecosystem discovery by mapping tools, workflows, build systems, and dependencies
• Build the business case for convergence by quantifying dual-ecosystem costs
• Identify AI acceleration opportunities
• Communicate to leadership: translate technical concepts into briefings

Staff Product Manager, AI Developer Productivity

• Design, build, test, deploy, and operate AI agents for customers using a combination of low-code and pro-code approaches
• Develop customer-facing AI solutions across the full ALM lifecycle, including design, development, testing, deployment, and ongoing iteration
• Build and integrate multi-model AI agents, selecting and orchestrating models based on use case, performance, and cost considerations
• Design and implement Retrieval-Augmented Generation (RAG) solutions, including document ingestion, vector databases, indexing strategies, and retrieval logic
• Configure and integrate MCP servers and related AI infrastructure components required for secure, scalable agent execution
• Implement secure authentication and authorization patterns for AI agents, including identity, permissions, and service-to-service access
• Collaborate with customers to understand business requirements and translate them into scalable AI agent designs
• Apply sound engineering practices including version control, environment management, testing strategies, and deployment automation
• Troubleshoot and optimize AI agents for performance, reliability, and accuracy
• Partner closely with security, data, and adoption teams to ensure AI solutions are safe, compliant, and aligned with governance requirements
• Translate the engineering work into customer enablement — designing and delivering technical training, workshops, labs, and demonstrations that help business users adopt the AI solutions you build
• Deliver enablement sessions both virtually and on-site, adapting depth and language for executive, technical, and frontline audiences
• Document architectures, designs, and operational considerations as part of customer deliverables and enablement assets

• Platform Architecture: Design and develop the core backend services, APIs, and microservices that enable product teams to easily and securely leverage AI models.
• Agent Orchestration: Build scalable frameworks and tools to support multi-step agent workflows, including task decomposition, tool invocation, and persistent memory.
• Evaluation & Reliability: Build robust evaluation systems to continuously measure reasoning quality, hallucination rates, and task success.
• Operational Excellence: Architect high-performance serving infrastructure with strict guarantees around latency, throughput, cost-efficiency, and error handling.
• Observability & MLOps: Ensure comprehensive monitoring, structured logging, and distributed tracing across all deployed AI models.
• Collaboration: Partner with designers, product managers, and other engineers to build self-serve infrastructure that transforms our AI development cycle.
• Leadership: Advocate for software engineering best practices, conduct thorough design and code reviews, and mentor junior engineers.

Senior Software Engineer, AI Platform

• Building production-grade AI applications and turning complex business challenges into practical, scalable solutions.
• Working with C#/.NET, Azure and modern LLM technologies.
• Collaborating with product, engineering, QA, UX and business stakeholders.
• Raising AI engineering capability across the wider technology team through coaching, practical standards and technical leadership.
• Shipping meaningful AI-enabled products in short iterations, learn from real usage and continuously improve quality.

Senior AI Engineer

• Embed directly with clients to discover operational pain points, map workflows, and identify high-ROI opportunities for AI and automation.
• Design and build AI agents using frameworks such as LangChain, LangGraph, AutoGen, or similar, including tool-use, memory, and multi-agent orchestration patterns.
• Develop AI-powered workflows that integrate with clients' existing systems (CRMs, ERPs, cloud platforms, communication tools, etc.)
• Engineer automation solutions across areas such as document processing, data extraction, reporting, customer communication, and internal operations.
• Own the full delivery lifecycle from scoping and architecture to build, testing, handoff, and lightweight documentation.
• Train and enable client teams to maintain and iterate on solutions post-deployment.
• Serve as a trusted technical advisor, translating complex AI capabilities into clear business value for non-technical stakeholders.
• Contribute internally by codifying reusable components, playbooks, and delivery templates that scale across the practice.