Machine Learning Engineer

BT-145 – Machine Learning Engineer
Location: Dulles (fully on-site, no remote option)

**MUST HAVE A POLY CLEARANCE TO APPLY. Those without a Poly clearance will not be considered.**

• Design, implement, and maintain scalable backend services and APIs in a containerized cloud environment (AWS preferred)
• Build mission-critical production applications focused on data discovery, analysis, and secure data delivery
• Integrate with cloud services and data platforms to expose high-value data through secure, performant interfaces
• Contribute to application features that integrate LLMs, agents, or ML models into production systems
• Collaborate in a Lean Agile environment with teammates and stakeholders, participating in code reviews, system design, and continuous improvement
• Work with CI/CD pipelines, modern build tools, and testing frameworks to ensure quality, security, and delivery speed
• Monitor and improve the performance and reliability of services, APIs, and data-driven components

• Strong Python application development skills with experience in modern frameworks (FastAPI preferred; Flask, Django acceptable)
• Experience designing and implementing scalable, maintainable, and OOP-based software in distributed systems
• Curiosity in LLM prompt engineering, context engineering, or agentic applications
• Proficiency with source control (Git) and CI/CD pipelines (AWS CodeBuild preferred, Jenkins, GitLab CI, GitHub Actions)
• Familiarity with DevSecOps practices, containerization (Docker, Kubernetes), and cloud infrastructure
• Experience with testing frameworks (PyTest preferred; unittest acceptable)
• Experience with Python project and dependency management tools (poetry preferred; uv, make, pip, conda acceptable)
• Effective written and verbal communication skills for technical collaboration

• Experience with agents or LLM workflows: prompt engineering, data pipelines, agents/multi-agent workflows (LangChain, LangGraph)
• Familiarity with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch) and NLP libraries (e.g., spaCy, Hugging Face Transformers)
• Hands-on with MLOps or model-serving tools (e.g., MLflow, SageMaker, Kubeflow)
• Familiarity with observability stacks (Prometheus/Grafana preferred; CloudWatch, ELK/EFK acceptable)
• Experience with event-driven and streaming systems (Kafka, Kinesis, SQS/SNS, AWS Step Functions)
• Knowledge of Infrastructure as Code (Terraform) and modern deployment pipelines
• Contributions to open-source projects, community efforts, or personal projects