Role Description The AI/ML Delivery Engineer is a hands-on senior practitioner who designs, builds, and scales enterprise AI, ML, and data products across cloud environments. This role combines deep engineering expertise with solution architecture, delivery leadership, and executive-level communication. This role supports the NIH mission by delivering secure, scalable, and innovative AI and data solutions that accelerate biomedical research, improve operational efficiency, and enable data-driven decision-making to advance human health. MEANINGFUL WORK AND PERSONAL IMPACT - The ideal candidate can move from strategy to production implementation across intelligent search, generative AI chat, agentic workflows, predictive ML, computer vision, and medical AI imaging. - They bridge data science, software engineering, platform engineering, and security to deliver robust, governed, cost-aware, fit-for-mission AI solutions. Solution Design and Delivery - Architect and deliver end-to-end AI/ML and generative AI solutions across the full lifecycle, including data integration, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement. - Design scalable, secure, and governed AI/ML architectures that support enterprise data management, cloud-native services, distributed computing, and high-performance workloads. - Develop and deploy production-ready AI solutions using modern techniques for large language models, intelligent search, workflow automation, model evaluation, monitoring, and continuous optimization. - Integrate enterprise AI and cloud services with organizational data platforms, security controls, governance frameworks, and operational processes. - Deliver AI and machine learning solutions across a variety of domains, including natural language processing, computer vision, predictive analytics, intelligent search, multimodal AI, and other mission-focused applications, using industry-standard frameworks and tools. - Design secure application programming interfaces (APIs), integration services, data pipelines, and orchestration workflows that enable AI capabilities to be reliably consumed by enterprise systems and end users. Technical Leadership, Governance, and Delivery Excellence - Serve as the AI/ML technical authority for cross-functional data science, engineering, platform, security, infrastructure, and business teams. - Lead architecture reviews, AI readiness assessments, performance benchmarking, and infrastructure trade-off analysis for large-scale cloud, GPU, and distributed data workloads. - Establish MLOps, LLMOps, and ModelOps practices for CI/CD, experiment tracking, model registry, prompt/model versioning, automated testing, deployment promotion, rollback, drift/quality monitoring, lineage, and cost optimization. - Define responsible AI controls for privacy, IAM, key management, audit logging, PHI/PII handling, explainability, model cards, bias/risk assessment, human oversight, and agentic guardrails. - Mentor engineers and data scientists while communicating complex AI concepts through clear solution narratives, architecture diagrams, demonstrations, and executive-ready materials. - Support solutioning activities by shaping technical approaches, estimating delivery patterns, and contributing to client responses. Qualifications - Education: Master of Science in Computer Science, Information Technology, Engineering, Mathematics/Statistics, Bioinformatics, Data Science, or equivalent professional experience. - Experience: 10+ years of related experience. - Hands-on experience with modern enterprise data platforms and cloud-native data architectures, including distributed data processing, data governance, machine learning lifecycle management, and batch and streaming data pipelines. - Production experience developing, deploying, and supporting AI/ML and generative AI solutions, including intelligent search, large language model (LLM) applications, workflow automation, model serving, evaluation, monitoring, and continuous improvement. - Experience with modern machine learning and deep learning frameworks, libraries, and tools for developing, training, evaluating, and deploying AI/ML solutions. - Experience designing, deploying, and managing AI/ML solutions in one or more major cloud environments, including cloud-native AI services, identity and access management, networking, security, and infrastructure. - Experience working with large, complex, sensitive, or regulated datasets, including data integration, migration, quality management, analytics, visualization, and governance in government, healthcare, or other regulated environments. - Excellent written and verbal communication skills, with the ability to collaborate effectively with technical teams, business stakeholders, and executive leadership. - Security clearance level: Must be able to obtain a Tier 3 Public Trust. - Must be a US Person. Requirements - This position is fully remote; If selected, travel to NIH at applicant's expense will be required for onboarding. Benefits - Growth: AI-powered career tool that identifies career steps and learning opportunities. - Support: An internal mobility team focused on helping you achieve your career goals. - Rewards: Comprehensive benefits and wellness packages, 401K with company match, and competitive pay and paid time off. - Flexibility: Full-flex work week to own your priorities at work and at home. - Community: Award-winning culture of innovation and a military-friendly workplace.

• Define, design, and supervise the technical architecture of solutions based on intelligent agents and LLMs, integrating tools such as LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent frameworks. • Implement MCP (Model Context Protocol) and A2A (Agent-to-Agent) architectures to enable multi-agent coordination and autonomous flows within business environments. • Work with the MLOps team and execution environments that enable continuous agent updating and deployment, including memory management, context, and long-term planning. • Collaborate closely with product, UX, data, and backend teams to map business needs to intelligent agent architectures.

• Design, develop, test, and maintain full-stack web applications. • Develop backend APIs and services using modern software engineering practices. • Build responsive frontend interfaces using contemporary JavaScript frameworks. • Utilize agentic AI development tools (e.g., Claude Code, Codex, GitHub Copilot, Cursor, or similar) to accelerate software development while maintaining high code quality. • Design and implement AI-powered workflows, automation, and intelligent agents. • Contribute to cloud-native application deployment and infrastructure. • Assist with containerization, CI/CD pipelines, and DevOps automation. • Support AI infrastructure, including model deployment, vector databases, retrieval systems, and GPU-enabled workloads. • Participate in software testing, quality assurance, and code reviews. • Collaborate with cybersecurity researchers to integrate AI into real-world security applications. • Document software designs, APIs, and engineering processes.

• Embed with a functional department for a defined engagement, running discovery to understand workflows and pain points before writing a line of code. • Scope, design, and build full-stack custom applications (not just automations or scripts) using modern frameworks, AI coding assistants, and internal platform APIs, moving from prototype to production quickly. • Own delivery end to end including architecture, build, testing, deployment, and the training and handoff that gets a department to actually adopt what you built. • Work hands-on in the code alongside AI coding tools (Claude Code, Codex, etc.) to compress build cycles, iterating with stakeholders in near real time rather than through long requirements documents. • Make explicit trade-offs between scope, speed, and quality as requirements shift, and communicate those trade-offs clearly to stakeholders and leadership. • Partner with IT, Security, and Data teams so every deployed application meets SentinelOne's standards for data security, compliance, and long-term supportability. • Codify recurring patterns, components, and playbooks from each engagement into reusable building blocks for the next department and for the broader engineering organization. • Feed field learnings back to platform, product, and data teams covering what worked, what broke, and what the next generation of internal tools should look like. • Measure and report success through production adoption and measurable business impact, not lines of code shipped or tickets closed.