# Machine Learning Engineer
The Machine Learning Engineer is responsible for the end-to-end development, deployment, and maintenance of machine learning (ML) and artificial intelligence (AI) solutions. This role requires a strong blend of data science, software engineering, and MLOps expertise to build robust, scalable, and secure AI/ML systems that address complex business challenges.
## Responsibilities
- Apply established machine learning and AI techniques to new problems and datasets
- Build, optimize, and maintain machine learning and AI models and supporting pipelines
- Evaluate and monitor ML/AI system outcomes, model performance, and data quality; define appropriate metrics and acceptance criteria
- Identify issues in models, pipelines, and datasets; recommend and implement improvements
- Design, develop, test, document, refactor, and maintain moderately complex programs/scripts to support ML development and deployment
- Follow agreed engineering standards, tools, and best practices to deliver secure, reliable, and maintainable solutions
- Monitor progress, report status, and communicate risks, blockers, and dependencies in a timely manner
- Collaborate with teammates through code reviews, design reviews, and shared ownership of deliverables
- Elicit requirements for ML/AI lifecycle practices, working methods, and automation (e.g., CI/CD, testing, deployment, monitoring)
- Select and implement appropriate lifecycle practices for components and microservices within the ML/AI solution
- Deploy automation to support well-engineered, repeatable, and secure build/release processes
- Define ML/AI modules needed for integration builds and produce build definitions for each release/generation of the solution
- Validate and accept completed ML/AI modules against agreed functional, quality, and performance criteria
- Apply data science techniques to new problems and datasets, using specialized programming approaches where needed
- Identify and implement opportunities to improve training data, features, and model performance
- Build and maintain data pipelines using data engineering standards and tools (ETL/ELT)
- Support monitoring of emerging technologies and contribute to internal reports, technology roadmaps, and knowledge sharing