AI Native Engineer – Center for Advanced Analytics, Visualization

📋 Description

• Collaborate closely with internal teams and clients to aid in the design, development, and deployment of AI-powered solutions across various sectors including health, energy, and education.

• Assist in translating business and research requirements into technical implementations, while building and refining agentic workflows and supporting the delivery of scalable, secure systems within enterprise environments.

• Contribute to the development of new systems and enhancements that integrate with existing infrastructure, working alongside colleagues and partners to deliver practical, production-ready solutions.

• Support the creation of AI systems and enhancements that align with RTI’s research infrastructure—such as survey data collection platforms, systematic review pipelines, data quality assurance systems, and proposal development workflows—by collaborating closely with data scientists, domain researchers, and software developers to ensure solutions meet the expected rigor in federally regulated and research-driven environments.

• Design, construct, and maintain AI-enabled systems and agentic workflows, focusing on retrieval, tool usage, orchestration, and iterative improvement, with an emphasis on production readiness and maintainability.

• Contribute to AI systems that integrate with and enhance RTI’s existing platforms—like survey data collection, systematic review pipelines, data quality assurance tools, and proposal workflows—while adhering to established architectures and constraints.

• Develop and deploy cloud-native services utilizing APIs, containers, and modern CI/CD practices, contributing to scalable, secure, and cost-effective AI solutions in enterprise environments.

• Translate research, business, and client needs into functional AI applications that automate or enhance workflows, working closely with data scientists, researchers, and software engineers to ensure practical value and methodological rigor.

• Contribute to testing and evaluation methodologies for AI systems, including performance monitoring, reliability assessments, and cost/quality trade-off analyses suitable for federally regulated and research-driven contexts.

• Engage in technical discussions, workshops, and prototype development with internal teams and clients, aiding in requirement gathering, solution demonstration, and iteration based on stakeholder feedback.

• Document systems, contribute to reusable components and shared abstractions, and promote internal knowledge sharing to enhance RTI’s AI engineering practices over time.

⛳️ Requirements

• Master’s degree and a minimum of three years of relevant work experience in building cloud-native applications such as APIs, microservices, containerization, or serverless functions; OR a Bachelor’s degree with at least five years of relevant experience in the same areas.

• A minimum of two years of programming experience in Python, JavaScript, TypeScript, or other similar languages commonly used in AI and backend development.

• Proven experience in the development and deployment of AI- or LLM-based systems, including retrieval pipelines, prompt design, agentic workflows, or workflow automation.

• Familiarity with at least one major AI platform or model provider (e.g., Anthropic Claude, OpenAI, Google Vertex AI, or open-source model serving).

• Experience in developing systems that handle sensitive, restricted, or confidential data, along with an understanding of how data access, privacy, and compliance requirements shape system design.

• Proficient with shared version control (e.g., Git) and collaborative software development workflows.

• Experience contributing to AI-enabled or agentic systems operating in production or research settings.

• Understanding of modern deployment and delivery practices, including CI/CD pipelines, containers, and basic infrastructure-as-code concepts.

• Familiarity with essential observability practices for deployed services, such as logging, monitoring, and tracking performance or cost signals for AI-enabled systems.

• Experience working in cross-functional, team-oriented environments, including participation in technical discussions, reviews, and iterative delivery.

• Capability to contribute to AI system design and implementation within established architectures.

• Strong written and verbal communication skills, with the ability to articulate AI system behavior, trade-offs, and limitations to non-technical researchers, clients, and federal stakeholders.

🏝️ Benefits

• Competitive base salary

• Generous paid time off policy

• Merit-based annual increases

• Bonus opportunities

• Robust recognition program

• Competitive range of insurance plans (including health, dental, life, and short-term and long-term disability)

• Access to a retirement savings program such as a 401(k) plan

• Paid parental leave for all parents

• Financial assistance with adoption expenses or infertility treatments

• Financial reimbursement for education and developmental opportunities

• Employee assistance program

• Numerous other offerings to support a healthy work-life balance