Key skills
PythonSQLAIMachine LearningPyTorchTensorFlowscikit-learnMLOpsMLflowKubeflowETLELTDatabricksAWSAzureGCPKubernetesDockerCI/CD
About this role
MetroStar is dedicated to building exceptional teams and delivering technology services. In this role, you will design, develop, and deploy machine learning solutions for secure web-based applications, collaborating with cross-functional teams to enhance production capabilities.
Responsibilities:
- Design, develop, implement, and fine-tune AI and machine learning models to support web-based applications in secure environments with evolving use cases
- Build and maintain data pipelines, training workflows, and experimentation environments to enable rapid model iteration and evaluation
- Evaluate model performance using quantitative and qualitative metrics (e.g., accuracy, robustness, stability, efficiency, generalization) and translate results into actionable improvements
- Analyze data, model outputs, and experimental results to recommend changes to algorithms, features, data sources, or system architecture
- Proactively identify and assess tools, frameworks, and technologies that best support platform goals, balancing performance, scalability, and maintainability
- Collaborate closely with software developers, data engineers, DevSecOps teams, and stakeholders to integrate AI capabilities into production systems
- Ensure AI and data science solutions are transparent, testable, and maintainable to support long-term operational use
- Communicate technical approaches, assumptions, tradeoffs, and results clearly to both technical and non-technical audiences, including during design reviews and demonstrations
Requirements:
- An Active Secret security clearance
- Bachelor's degree in Computer Science, Engineering, Data Science or related technical discipline
- 4+ years of experience building and managing ETL and ELT data pipelines within Databricks environment
- Hands-on experience with Python and SQL and libraries such as TensorFlow, PyTorch, Scikit-learn
- Experience deploying models on cloud platforms (such as AWS, GCP, Azure) and containerization tools (Docker, Kubernetes)
- Experience managing model deployment and monitoring (MLOps, MLflow, Kubeflow, etc.)
- Knowledge of data modeling, neural network architectures, and software development and CI/CD best practices
- Must be willing/able to travel to customer, as needed