AI/ML Engineer
FTI - Frontier Technology Inc.
full-time
Posted on:
Location Type: Hybrid
Location: Tampa • Florida • 🇺🇸 United States
Visit company websiteJob Level
Mid-LevelSenior
Tech Stack
PythonPyTorchScikit-LearnTensorflow
About the role
- 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 secruity 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).
- Familiarity with DoD/IC AI assurance, security, and deployment environments.
- Experience fine-tuning and evaluating LLMs or smaller task-specific models using LoRA, QLoRA, or PEFT.
- 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.
- Experience integrating AI capabilities into production systems or mission applications.
- 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 required.
Benefits
- Telecommute
- Competitive salary
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
AI/ML modelsMLOps pipelinesPythonfine-tuningvector databasesprompt engineeringretrieval architecturesagent-based applicationsfeature engineeringinference services
Soft skills
collaborationcommunicationpeer reviewsdocumentationproblem-solving
Certifications
Bachelor's degreeMaster's degreeSecret clearance