What are the responsibilities and job description for the Machine Learning Engineer position at BTI360?
Join us in transforming a manual content review process into a streamlined, self-service solution using Generative AI. We’re automating the review of resumes, presentations, and books by applying AI to classification and release rules—cutting down review time and increasing efficiency. If you’re excited about applying AI to make real-world processes faster and smarter, this mission is for you.
What you will do in this role:
- 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
- Work closely with teammates and stakeholders in a Lean Agile environment to build mission-critical production 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
You may thrive in this role if you have the following skills:
- Active TS/SCI with Polygraph
- 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
- Experience with source control (e.g. Git) and CI/CD pipeline tools such as AWS CodeBuild (preferred), Jenkins, GitLab CI, or GitHub Actions
You may excel in this role if you have the following skills:
- 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
- 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