AI/ML Engineer

Frontier Technology Inc. (FTI) is seeking a hands-on AI/ML Engineer to design, build, and deploy advanced machine learning solutions supporting defense and national security missions. The role involves developing AI/ML models and pipelines, collaborating with engineers and analysts, and ensuring models are production-ready.

Responsibilities:

• Design, develop, and deploy AI/ML models and pipelines that meet mission and performance objectives
• Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain
• Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or custom training/inference orchestration)
• Implement and optimize vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval architectures (RAG, graph, hybrid)
• Write clean, efficient Python code for data ingestion, feature engineering, embeddings, and inference services
• Experiment with fine-tuning and optimization of LLMs and task-specific models (LoRA, QLoRA, PEFT)
• Contribute to agent-based applications using frameworks like LangGraph, AutoGen, CrewAI, or DSPy
• Integrate AI services into real-world systems via APIs, event-driven workflows, or UI copilots
• Collaborate with data engineers, software developers, and mission analysts to ensure AI models are production-ready and aligned with customer needs
• Participate in peer reviews, contribute to shared repositories, and document models and experiments for reproducibility

Requirements:

• Must be a U.S. citizen and be willing to obtain and maintain a security clearance, as needed
• 6+ years of professional experience developing and deploying AI/ML solutions in production environments
• Minimum of 3 years' professional experience within the Department of Defense/Department of War (DoD/DoW) AI assurance, security, and deployment environments
• Strong Python development skills with hands-on experience building AI/ML solutions
• Direct experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, or LangChain
• Proven ability to build and deploy MLOps pipelines using MLflow, Kubeflow, DVC, or equivalent
• Working knowledge of vector databases (Milvus, Pinecone, Chroma, FAISS) and retrieval-based architectures (RAG, hybrid, graph)
• Professional experience fine-tuning and evaluating LLMs or smaller task-specific models using LoRA, QLoRA, or PEFT
• Professional experience integrating AI capabilities into production systems or mission applications
• Familiarity with agentic frameworks (LangGraph, AutoGen, CrewAI, DSPy) and multi-agent reasoning
• Understanding of prompt engineering, retrieval quality, and grounding methods
• Exposure to GPU-based or edge inference environments
• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field
• Active Secret clearance preferred; ability to obtain one is required