Distinguished AI/ML Engineering Lead
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Job Description
AI/ML Engineering Lead at Frontier Technology delivering mission-focused solutions with scalable AI systems. Responsible for architecture, deployment, and mentorship within secure environments.
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.
- Collaborate across engineering, data, and modeling teams to unify FTI’s AI portfolio, ensuring interoperability and reuse across mission systems.
Requirements:
- 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.
- Active Secret clearance required; TS/SCI strongly preferred.
- Strong communication and mentoring skills, with the ability to lead technically while remaining deeply hands-on.
Benefits:
- Telecommute
- Technical leadership and mentorship
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