Machine Learning Engineer

• It is potentially ready to be used for income simulation forecasting that has been manual and now they can automate and use this model as a potential input.
• If the model is accurate enough and they can explain it well enough they can push this.
• This is a lot more about technical expertise and technical leadership than strategic. More Engineer.
• AWS Sagemaker. Switching from AWS to Databricks. Expertise in more than one tool is helpful. AWS and Databricks. LLM. Cloud formation Lambda. Python. Pyspark AWS Glue. ML Ops Sagemaker.
• We had a very strong profile submitted that the team really liked, but they identified a candidate another candidate with more hands on keyboard experience in ML Ops in Sagemaker.
• More senior, experienced ML Engineer that has built similar models and is familiar with other tech ologies, including AWS SageMaker and Databricks (currently using AWS Forecast), Jupyter Notebook, AWS Bedrock, XGBoost.
• AWS Suite
• Python
• Amazon ECR
• Version Control
• Integration with ADO
• Building, enhancing, explaining, risk and audit experience to understand controls.
• Looking at model optimization and metrics and importance. How to incrementally improve those. Getting the optimized version. Good stakeholder engagement experience.
• Data ingestion: RDS database, SQL Server, FRED Data (Federal Reserve Economic Data), MSRP . org (Municipal Securities Rulemaking Board).
• Onsite Tuesday and Thursday ideally, but they can be flexible if someone wants other days., * Collaborate with data scientists, software engineers, and DevOps teams to develop and deploy ML models
• Build, test, and deploy ML Ops pipelines on AWS
• Manage and monitor production ML systems to ensure optimal performance, reliability, and scalability
• Design and implement automated workflows for data cleaning, feature engineering, model training, and model deployment
• Develop and maintain documentation for ML Ops processes and procedures
• Continuously improve ML Ops pipeline performance and efficiency
• Troubleshoot and resolve issues related to ML model performance, data quality, and infrastructure

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• Minimum of 5-7 years of experience in ML Ops, DevOps, or related roles
• Strong knowledge of AWS services and tools related to ML Ops, such as SageMaker, Step Functions, Lambda, and CloudFormation
• Hands-on experience building and deploying ML models in production using AWS
• Proficiency in Python and/or other programming languages commonly used in ML, such as R, Java, or Scala
• Familiarity with containerization technologies such as Docker and Kubernetes
• Excellent problem-solving skills and attention to detail
• Ability to work independently as well as in a team environment
• Strong communication skills and ability to explain technical concepts to non-technical stakeholders

Knowledge, Skills, Abilities and Behaviors:

• Knowledge of machine learning concepts, algorithms, and frameworks.
• Knowledge of software engineering principles and best practices, such as version control, continuous integration, and agile development methodologies.
• Strong understanding of data analysis and data manipulation techniques.
• Ability to design and implement scalable, secure, and fault-tolerant ML Ops pipelines on AWS.
• Ability to analyze and interpret data to identify patterns, trends, and anomalies, using advanced data manipulation techniques.
• Outstanding communication skills (verbal, written, visualization, and listening).
• Self-starter who can work independently as well as in a team setting.
• Hands-on technologist with the ability to help drive the strategy and mentor others.
• Giving and receiving effective feedback across all interactions.
• Interest in understanding customer perspectives to aid in the development of the right solution.
• Interest in understanding business needs to aid in developing solutions that are right for the broader organization.