• Architect and integrate hybrid AI systems that combine traditional machine learning, deep learning, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines. • Design and deploy scalable AI architectures including APIs, microservices, and model-serving frameworks that integrate seamlessly with analytic, simulation, or operational systems. • Lead the full AI/ML lifecycle — from data ingestion and feature engineering through training, deployment, and sustainment within secure DoD environments (IL5/IL6, ATO, GovCloud). • Engineer event-driven data pipelines and feature stores for both structured and unstructured data, including text, imagery, and simulation outputs. • Ensure Responsible AI practices by embedding traceability, explainability, and confidence scoring into deployed systems. • Implement and maintain MLOps pipelines (MLflow, Kubeflow, Airflow, Docker/Kubernetes) to support continuous integration, retraining, and drift detection. • Transition R&D prototypes into production, optimizing for mission constraints such as limited compute, edge environments, or disconnected operations. • Collaborate across engineering, data, and modeling teams to unify FTI’s AI portfolio, ensuring interoperability and reuse across mission systems.

• Design, develop, test, maintain, and improve an integrated codebase of AI and machine learning tools • Work with data scientists, researchers, product teams, and other domain experts to build solutions for complex data-oriented problems • Develop solutions for advancing real-world preclinical discovery programs, tackling problems such as ADME modeling, toxicity prediction, and generative chemistry.

• Lead to the full range of development stages for AI/ML projects, from early explorations to productization and maintenance. • Work closely with data scientists and network security experts to design and implement scalable ML pipelines for large datasets. • Design and implement strategies to deploy LLMs and for fine-tuning. • Conduct experiments, analyze results and resource consumption using various metrics and visualization techniques. • Drive adoption of ML development best practices in code health, quality, test stability and maintainability. • Participate in technical discussions within the Labs team and collaborate with other teams across the organization. • Work in an Agile development team focused on exploring and delivering ML use cases.

• Development and deployment of LLM-powered features, including summarization tools and clinician-facing conversational agents • Build backend services in Python that integrate ML/LLM models with Fullscript’s platform • Implement prompt engineering techniques to optimise model outputs • Implement AI feature quality testing, CI/CD pipelines, and version control for all model-related workflows • Collaborate with medical and product teams to deliver AI features for practitioners and patients • Work cross-functionally with engineering, analytics, and medical SMEs to refine requirements and ensure data solutions support clinical contexts • Stay current with the latest LLM research and emerging AI technologies