Junior AI/ML Engineer supporting data preprocessing and model evaluation at Node.Digital. Collaborate on AI/Machine Learning efforts with government and commercial clients.
About the role
- Senior Machine Learning Engineer at Collinson focusing on cloud-based ML pipelines development. Leading the architecture and deployment of ML and AI solutions in a hybrid environment.
Responsibilities
- Lead the architecture, design, and implementation of robust, scalable, and high-performing ML and AI platforms.
- Design and develop end-to-end ML workflows and pipelines using AWS SageMaker, Python, and distributed computing technologies.
- Hands-on implementation of parallel computing and distributed training methodologies to enhance the efficiency and scalability of machine learning models.
- Collaborate closely with data scientists and engineers to deploy complex ML and deep learning models into mission-critical production systems.
- Ensure best practices in CI/CD, containerization, orchestration, and infrastructure-as-code are consistently applied across platforms.
- Foster a culture of innovation, continuous improvement, and self-service analytics across the team and organization.
- Stay abreast of latest advancements in ML and AI technologies, proactively applying new techniques and tools to deliver superior outcomes.
Requirements
- Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or related fields
- 7+ years of proven hands-on experience building ML/AI platforms, especially involving AWS SageMaker and distributed computing frameworks
- Deep expertise in designing and deploying ML/AI platforms, specifically using AWS SageMaker
- Strong proficiency in Python and its ecosystem (e.g., TensorFlow, PyTorch, scikit-learn)
- Extensive hands-on experience with parallel computing frameworks and distributed processing
- Proficient in SQL, ETL, data warehousing, and data modeling techniques
- Thorough understanding of statistical analysis, predictive modeling, and data mining methodologies
- Proven capability of deploying machine learning models into production at scale
- Familiarity with CI/CD pipelines, containerization (Docker), and version control (Git).
- Excellent communication skills, capable of clearly articulating technical concepts to diverse audiences.
Benefits
- Flexible working hours
- Professional development opportunities
- Health insurance
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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