• Data mining, data cleaning, data engineering. • Select features, build, and optimize classifiers for the use of machine learning techniques. • Develop new ML algorithms to find predictive patterns. • Establish meaningful criteria for evaluating algorithm performance and suitability. • Automate model training and testing and deployment via machine learning continuous delivery pipelines. • Implement working, scalable, production-ready Machine Learning and AI Process Automation models and code. • Optimize processes for maximum speed, performance and accuracy. • Keep up to date with Machine Learning best practices and evolving open source frameworks. • Work in an agile team in a scrum process, collaborating closely with software engineers, data scientists, data engineers, subject domain experts and QA analysts.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description FTI is seeking a Distinguished AI/ML Engineer to serve as a technical leader, architect, and integrator — designing, building, deploying, and sustaining AI systems that transform complex mission data into trusted, explainable insights. This is a hands-on builder role, not an analytics management position. The ideal candidate is equally comfortable writing model code, standing up ML pipelines, and integrating AI inference services into operational systems within secure environments. The right candidate blends deep AI/ML engineering expertise with system-level architecture leadership and an ability to unify data engineering, simulation modeling, and responsible AI principles into scalable, mission-ready capabilities. Responsibilities - 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. - Provide technical leadership and mentorship, setting standards for model quality, architectural design, and ethical AI deployment across programs. - Collaborate across engineering, data, and modeling teams to unify FTI’s AI portfolio, ensuring interoperability and reuse across mission systems. - Support proposal and solution development, providing technical inputs for AI/ML architectures, data strategies, and Responsible AI assurance frameworks. Qualifications - Preferring candidates close to Dayton OH, Chesapeake VA, Huntsville AL, or Colorado Springs CO. Or willing to travel to these locations and customer sites, as needed. - Bachelor’s degree in Computer Science, Engineering, or a related technical field (Master’s or Ph.D. preferred). - 10+ years of overall experience in AI/ML development, with 5+ years designing and deploying scalable AI/ML architectures, including at least two full lifecycle implementations (from prototype to operational system). - Proficiency in Python, PyTorch, TensorFlow, and modern ML frameworks. - Experience designing or deploying systems using vector databases (Milvus, Pinecone, Weaviate), knowledge graphs, and semantic search frameworks. - Proven ability to design event-driven data pipelines using Databricks, Spark, Flink, or Kafka. - Demonstrated experience deploying AI/ML systems in secure, classified, or edge environments. - Familiarity with Responsible AI and assurance principles, including bias detection, explainability, human-machine teaming, and hallucination prevention. - Experience integrating AI models into simulation, modeling, or operational planning systems is highly desirable. - Experience transitioning R&D systems into accredited production environments. - Active Secret clearance required; TS/SCI strongly preferred. - Strong communication and mentoring skills, with the ability to lead technically while remaining deeply hands-on.

• Build, maintain, and optimize Upstart’s next-generation machine learning and simulation platform, enabling increased scale, performance, and confidence in decisioning • Develop high-quality software applications that enable machine learning models to be applied to the ever-evolving needs of the business • Enable the modernization of our serving infrastructure, reducing inference latency to just a few seconds for our most complex models • Design and contribute to our simulation systems to more accurately reflect production environments, reducing simulation cost and enabling broader usage across teams • Communicate closely with cross-functional partners from ML, Engineering, Product, and Data Engineering teams, keeping all stakeholders informed • Mentor engineers across the team, sharing expertise on distributed systems, MLOps, and scalable architecture

• 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.