Hybrid AI/ML Engineer

at FTI - Frontier Technology Inc.

• Job title: AI/ML Engineer
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Python, PyTorch, Scikit-Learn, Tensorflow
• Location requirements: Hybrid, Dayton, United States

About the role

• AI/ML Engineer at Frontier Technology Inc. designing and deploying machine learning solutions for defense and national security missions. Focused on execution and building end-to-end pipelines.

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.
• 4–6 years of professional experience developing and deploying AI/ML solutions in production 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).

Benefits

• Telecommute
• No clearance requirements