Senior Machine Learning Engineer

Job Title: Senior Machine Learning Engineer

Department: Technology

About the Role: 

As a Senior Machine Learning Engineer you will work in a team of Machine Learning Engineers you will build and maintain the core infrastructure to allow data scientists to develop, train, evaluate, deploy and operate ML models and pipelines. This role reports to the Manager, Machine Learning Engineer, and consults with key business stakeholders as a subject matter expert on data engineering and data science solutions. 

What You'll Do:

Model Development and Deployment

• Automation and Efficiency: Streamline the entire model lifecycle, from initial development and training to deployment and retraining, using automation tools and best practices to accelerate model delivery and iteration.
• Timely Model Deployment and Scoring: Empower data scientists with the ability to rapidly deploy models and generate scores for evaluation, facilitating faster experimentation and model selection.
• Model Migration: Manage the migration of existing models from legacy systems to the AWS SageMaker platform, ensuring compatibility and leveraging cloud-based capabilities.
• Engineering Standards: Define and enforce engineering best practices for model development, deployment, and maintenance, ensuring code quality, scalability, and maintainability.
• Scoring Pipeline Optimization: Continuously optimize model scoring pipelines to eliminate errors, improve performance, and reduce latency.
• Resource Adjustment: Proactively adjust instance resources based on data scientist requirements, balancing cost and performance for optimal model development and deployment.
• Enable and operationalize Large Language Models (LLMs) for production use cases.

Data Collaboration and Management

• High-Quality Datasets: Collaborate with data engineers to manage and curate high-quality datasets for both internal and external use by downstream users and applications, ensuring data accuracy, completeness, and reliability.
• Infrastructure for ML Solutions: Actively participate in the development and maintenance of infrastructure to support scalable machine learning solutions, including data storage, compute resources, and model deployment platforms.
• Optimized Data Pipelines: Work closely with the Data Engineering team to design, implement, and maintain optimized data pipeline architectures that can efficiently handle large and complex datasets, ensuring data is processed and delivered in a timely manner.
• Data-Related Technical Support: Provide stakeholders with data-related technical support and solutions, addressing their data infrastructure needs and helping them leverage data effectively.
• Data Separation and Segregation: Maintain strict data separation and segregation practices in accordance with relevant data policies and regulations, ensuring data privacy, security, and compliance.
• Data Preparation Tools: Collaborate with data scientists to develop and maintain data preparation tools that streamline their workflows and enable them to efficiently prepare data for analysis and modeling.

Overall Goal: By fulfilling these responsibilities, the Sr. Machine Learning Engineer will play a key role in driving data-driven insights, optimizing processes, and developing innovative solutions that enhance Unite Us' products and services, ultimately improving outcomes for the organization and its clients.

You’re a great fit for this role if:

• You have at least 5+ years of experience as a Machine Learning Engineer, Data Scientist or Data Engineer
• At least 3+ years of experience in developing and implementing Machine Learning infrastructure and MLOps (Machine Learning Operations) within a cloud environment, specifically utilizing AWS Sagemaker and Snowflake
• Hands-on experience in building, optimizing, and maintaining data pipelines, architectures, and data sets to support machine learning initiatives
• Strong proficiency in Python, SQL and expertise in utilizing AWS automation tools for streamlining and automating processes
• Experience in implementing and utilizing monitoring and metrics systems to track and evaluate the performance of predictive models
• Experience in model monitoring and ML ops including containers and orchestration
• Prior experience prompting large language models and building RAG applications is a plus

Our Mission:

Unite Us’ mission is to unlock the potenti