Distinguished AI​/ML Engineering Lead

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, Gov Cloud).
• 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.

• Active Secret clearance required; TS/SCI strongly preferred.
• 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, Tensor Flow, 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.
• Strong communication and mentoring skills, with the ability to lead technically while remaining deeply hands‑on.