Responsibilities:
Develop and optimize complex data pipelines applying machine learning engineering principles to enhance efficiency and scalability.
Integrate and optimize data and model pipelines within production environments diagnosing data inconsistencies and documenting assumptions.
Employ experimental methodologies statistics and machine learning concepts to create self-running AI systems for predictive modeling.
Collaborate with data science teams to review model-ready datasets and feature documentation ensuring completeness and accuracy.
Perform data discovery and analysis of raw data sources applying business context to meet model development needs.
Comfort with exploratory data exploration and tracking data lineage during inception or root cause analysis.
Engage with internal stakeholders to understand business processes and translate requirements into analytical approaches.
Write and maintain model monitoring scripts diagnosing issues and coordinating resolutions based on alerts.
Serve as a domain expert in machine learning engineering on cross-functional teams for significant initiatives.
Stay updated with the latest advancements in AI/ML and apply them to real-world challenges.
Participate in special projects and additional duties as assigned.
Qualifications:
Undergraduate degree or equivalent experience; a graduate degree is preferred.
Minimum of 8 years of relevant work experience.
At least 3 years of hands-on experience designing ETL pipelines using AWS services (e.g. Glue SageMaker).
Proficiency in programming languages particularly Python (including PySpark PySQL) and familiarity with machine learning libraries and frameworks.
Strong understanding of cloud technologies including AWS and Azure and experience with NoSQL databases.
Familiarity with Feature Store usage LLMs GenAI RAG Prompt Engineering and Model Evaluation.
Experience with API design and development is a plus.
Solid understanding of software engineering principles including design patterns testing security and version control.
Knowledge of Machine Learning Development Lifecycle (MDLC) best practices and protocols.
Understanding of solution architecture for building end-to-end machine learning data
How We Work
Vanguard has implemented a hybrid working model for the majority of our crew members designed to capture the benefits of enhanced flexibility while enabling in-person learning collaboration and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.
Required Experience:
Senior IC
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