Aminuteman Technologies is a cutting-edge company specializing in Defense, Aerospace, AI, and Cybersecurity Solutions. Our mission is to develop next-generation autonomous systems, drone technology, and AI-driven defense innovations to enhance security and drive technological advancements. We are building solutions that shape the future of national defense and aerospace technology.
Lead the end-to-end development and deployment of AI/ML models for production use cases (e.g. predictive analytics, recommendation systems, computer vision, NLP).
Design, implement, and maintain FastAPI-based microservices for model serving, inference, and integration with broader systems.
Collaborate with data engineers, product managers, and domain experts to shape requirements, validate model outputs, and deliver business value.
Build and maintain DevOps / MLOps pipelines — from training to production deployment, including CI/CD, monitoring, logging, versioning, A/B testing, rollback strategies, etc.
Optimize models and APIs for performance, scalability, latency, and robustness.
Automate workflows for data ingestion, model retraining, evaluation, and drift detection.
Ensure high standards of code quality, documentation, and testability.
Stay updated with the latest in AI/ML research, tools, and best practices, and mentor junior engineers.
3 to 8 years of hands-on experience in AI/ML engineering or related roles.
Excellent proficiency in Python, and strong experience with ML libraries (e.g. PyTorch, TensorFlow, scikit-learn).
Demonstrated experience building APIs or microservices using FastAPI (or equivalent frameworks).
Solid knowledge of DevOps / MLOps tools and practices (Docker, Kubernetes, Terraform, CI/CD, monitoring, etc.).
Experience deploying models in cloud environments (AWS, GCP, Azure) or hybrid setups.
Strong understanding of data structures, algorithms, software engineering principles, and system design.
Experience with logging, metrics, and observability tools in production.
Effective communication skills, able to explain technical decisions to cross-functional stakeholders.
Hands-on experience with MLflow, Kubeflow, or other MLOps frameworks.
Exposure to NLP, computer vision, time series, or recommendation systems.
Experience with microservices architecture, messaging systems (Kafka, RabbitMQ), or event-driven design.
Contributions to open-source AI/ML or participation in research projects.
Familiarity with data governance, fairness, model explainability, and ethics in AI.
Work on impactful, cutting-edge AI/ML projects with real users.
Autonomy, ownership, and opportunities to lead technical initiatives.
Learning culture: support for conferences, courses, research, experimentation.
Competitive salary, benefits, and performance-based rewards.
A collaborative, supportive environment full of passionate technologists.