AI/ML Full Stack Engineer

We’re building a next-generation logistics, maintenance planning, and decision-support platform that helps maintenance planners anticipate risk, optimize schedules, manage inventory, and improve fleet readiness before problems impact operations.

This is not a greenfield project.

The foundation already exists. Core inventory systems, maintenance planning workflows, forecasting visualizations, reporting capabilities, and compliance requirements have already been established. We are entering the phase where advanced AI, machine learning, forecasting, optimization, and agentic workflows begin creating real operational value.

WHAT YOU’LL DO

• Develop workload forecasting, schedule-risk, optimization, and simulation models using Python, NumPy, SciKit-Learn, PyTorch, TensorFlow, or appropriate statistical methods.
• Build production features that connect model outputs to backend APIs, databases, and frontend visualizations.
• Implement Monte Carlo simulations, schedule optimization heuristics, workload leveling, uncertainty modeling, and critical-path analysis.
• Integrate multimodal embeddings, semantic search, vector databases, and retrieval-augmented workflows.
• Build agentic workflows using Pydantic AI, CopilotKit, AG-UI, MCP integrations, and tool-calling patterns.
• Collaborate with full-stack engineers to expose model results through FastAPI, PostgreSQL/pgvector, and React/TypeScript dashboards.
• Develop user-facing planning, forecasting, and decision-support features for maintenance scheduling, parts availability, AWP constraints, and workforce planning.
• Write tests and validation workflows for model behavior, backend APIs, and user-facing analytics.
• Support deployment of AI and forecasting features into containerized, Kubernetes-based, and potentially air-gapped environments.

WHAT YOU’LL BE BUILDING

You won’t be creating models that sit in notebooks.

You’ll be helping build production systems that combine:

• Forecasting and predictive analytics
• Schedule optimization and simulation
• Inventory intelligence
• Semantic search and retrieval
• Agentic AI workflows
• Real-time operational data
• User-facing decision support tools

The platform already includes inventory management, maintenance planning, forecasting visualizations, reporting, and modern full-stack infrastructure.

The next phase focuses on adding intelligence:

• AI-guided recommendations
• Semantic retrieval and vector search
• Optimization engines
• Agent tool-calling workflows
• Real-time data pipelines
• Advanced forecasting and simulation capabilities

TECHNOLOGY STACK

Frontend

• React
• TypeScript
• Modern planning and visualization interfaces
• Interactive forecasting and scheduling views

Backend

• Python
• FastAPI
• PostgreSQL
• SQLAlchemy
• pgvector

AI / Machine Learning

• PyTorch
• TensorFlow
• SciKit-Learn
• Embeddings
• Semantic Search
• Retrieval-Augmented Generation (RAG)
• Pydantic AI
• CopilotKit
• AG-UI
• Agent Tool Calling

Infrastructure

• Docker
• Kubernetes
• Containerized Deployments
• Secure Government Environments
• Air-Gapped Systems, You’ll work on forecasting, optimization, inventory intelligence, semantic retrieval, and AI-assisted workflows that transform complex operational data into actionable recommendations.

• B.S. degree in Computer Science, Engineering, Mathematics, Statistics, Operations Research, Data Science, or a related technical field.
• U.S. Citizenship and active Secret Clearance, or ability to obtain one where applicable.
• Strong proficiency in Python 3, NumPy, Pandas, and applied machine learning or statistical modeling.
• Experience with at least one major ML framework such as PyTorch, TensorFlow, or SciKit-Learn.
• Experience building production software beyond notebooks, including APIs, databases, tests, and deployment workflows.
• Familiarity with FastAPI, Pydantic, PostgreSQL, and modern backend development practices.
• Ability to translate domain constraints into practical forecasting, simulation, or optimization logic., * Experience with schedule optimization, operations research, Monte Carlo simulation, discrete-event simulation, or resource-constrained planning.
• Experience with PostgreSQL, pgvector, embeddings, semantic search, and retrieval-augmented generation.
• Familiarity with Pydantic AI, CopilotKit, AG-UI, MCP integrations, agentic coding, and agent harness utilization.
• Experience with React, TypeScript, TanStack Query, data visualization, dashboards, or full-stack feature development.
• Experience with Kubernetes, Helm-style deployments, Docker, and air-gapped environments.
• Knowledge of Navy maintenance processes, submarine systems, shipyard operations, depot maintenance, logistics, parts management, or workforce scheduling.
• Experience with model evaluation, experiment tracking, reproducibility, observability, and production monitoring.