Machine Learning Engineer (Polygraph Required)

BTI360 is a data discovery company, using AI/ML to transform proprietary data into actionable insights that directly support our customer's mission. We are seeking a Senior Machine Learning Engineer with the expertise, creativity, and leadership to drive innovative solutions that apply advanced AI techniques to solve complex problems. This is an opportunity to make a tangible impact by leveraging cutting-edge technology, data, and a deep understanding of mission-driven challenges.

In this role, you will:

• Work closely with teammates and stakeholders in a Lean Agile environment to build mission-critical applications focused on data discovery and analysis
• Participate in code reviews, system design discussions, and continuous improvement initiatives
• Leverage modern build tools, testing frameworks, and CI/CD pipelines to ensure quality and delivery speed
• Build and deploy machine learning models using containerization and cloud services
• Design and maintain data pipelines and model-serving infrastructure
• Monitor model performance and ensure reliability in production environments
• Collaborate with cross-functional teams to deliver end-to-end ML solutions

You might thrive in this role if you have the following skills:

• Active TS/SCI with Polygraph
• Experience with source control (e.g. Git) and CI/CD pipeline tools such as AWS CodeBuild (preferred), Jenkins, GitLab CI, or GitHub Actions
• Strong Python development skills with experience in ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch)
• Experience deploying models with tools like Docker, Kubernetes, and cloud ML services
• Ability to manage structured and unstructured data using SQL and scripting tools
• Effective written and verbal communication skills necessary to perform job duties and collaborate with team members

You may excel in this role if you have also these skills:

• Familiarity with monitoring and observability stacks such as Prometheus/Grafana (preferred), CloudWatch, or ELK/EFK
• Experience designing and implementing scalable, maintainable, and OOP based software in a containerized cloud environment (AWS preferred) leveraging foundational services for computing, identity management, and networking.
• Contributions to open-source libraries or community projects or personal projects
• Hands-on experience with MLOps tools such as MLflow, SageMaker, or Kubeflow
• Experience integrating models into software applications via APIs
• Understanding of model governance, versioning, and interpretability practices