Machine Learning Engineer

Responsibilities:

• Lead engineering design and deployment of complex data and model pipelines. Establish best practices and drive innovation in end-to-end data and model pipeline deployment and automation technologies. Expert-level knowledge in SDLC and related tools and processes. Proficient in developing highly efficient designs and strategies for both batch and real-time data pipelines.

• Integrate and optimize existing data and model pipelines. Identify and diagnose data inconsistencies and errors, document assumptions, and fill data gaps. Apply expert knowledge of experimental methodologies, statistics, optimization, probability theory, and machine learning concepts to create self-running AI systems for automating data science models.

• Partner with data science teams to understand data requirements and perform data discovery for model development. Perform detailed analysis of raw data sources for data quality, apply business context, and model development needs. Drive best practices and innovation in data discovery techniques. Write model monitoring scripts as needed. Diagnose root causes based on model monitoring alerts and triage issues. Coordinate and plan responses to model monitoring alerts and resolve issues.

• Demonstrate strategic thinking and apply expertise in cloud-based architectures, technologies (including GenAI), and platforms to deliver optimized ML models at scale.

• Drive engineering innovation in the labs through effective collaboration with data scientists, technologists, and business stakeholders. Drive roadmap/strategy discussions with product owners and business partners as a trusted advisor for engineering/technology.

• Serves as a machine learning engineering subject matter expert on cross functional teams for large strategic initiatives and contributes to the growth of the Vanguard analytic community. 

Qualifications:

• MS in Computer Science, Statistics, Machine Learning, Data Science, Electrical Engineering, or related field.

• Minimum of 5 years related work experience, with 2+ years of experience in deploying and maintaining AI/ML related projects.

• Advanced programming skills, particularly in languages like Python. Proficiency in using AWS machine learning services, such as Sagemaker, for model pipeline development, training, and deployment. Proven ability to develop and deploy machine learning models with robust data architectures.

• Proficiency in AWS Cloud Formation for infrastructure as code. Experience with CI/CD practices for both machine learning and data engineering workflows.

• Experience with data processing technologies, such as Apache Spark, AWS Glue, and Hadoop.

• Ability to convey technical concepts to diverse stakeholders and demonstrate thought leadership in engineering space. Strong technical skills on ML/AI with a proven track record.