Senior AI/ML Engineer

Vanguard-posted about 2 months ago
Full-time • Mid Level
Hybrid • Malvern, PA
5,001-10,000 employees
Funds, Trusts, and Other Financial Vehicles
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Vanguard's Corporate Services department is seeking a Senior AI/ML Engineer to design and deliver scalable machine learning infrastructure and pipelines that enable experimentation, deployment, and monitoring of AI/ML models across the enterprise. This role is ideal for someone with deep technical expertise in building production-grade ML systems and a passion for driving innovation through data and automation.

  • Architect and implement scalable, efficient, and reliable data and ML pipelines using best practices in machine learning engineering.
  • Build and maintain MLOps frameworks to support model deployment, monitoring, and lifecycle management in production environments.
  • Ensure data integrity, proactively identifying and resolving quality issues across data and model pipelines.
  • Collaborate with data scientists, solution architects, product managers, and Agile leads to align on technical direction and keep stakeholders informed.
  • Conduct exploratory data analysis and integrate business context to inform modeling strategies.
  • Track data lineage and perform root cause analysis during early-stage exploration or issue resolution.
  • Translate business requirements into scalable AI/ML solutions in partnership with internal stakeholders.
  • Implement and maintain model monitoring, including data and model drift detection, alerting, and resolution workflows.
  • Design and execute A/B testing, backtesting, and other validation strategies to assess model performance and business impact.
  • Anticipate ambiguity in data, requirements, or business context and devise creative, scalable solutions to address them.
  • Serve as a technical expert in machine learning engineering on cross-functional teams.
  • Stay current with advancements in AI/ML and assess their relevance to business challenges.
  • Bachelor's degree in Computer Science, Engineering, or related field (Master's preferred).
  • 8+ years of experience across machine learning engineering, data engineering, and MLOps implementation, including: Designing and deploying production-grade ML systems.
  • Building scalable data pipelines and ML workflows.
  • Managing model lifecycle in cloud environments.
  • Proficient in Python and familiar with ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
  • Strong understanding of cloud platforms, especially AWS SageMaker.
  • Experience with CI/CD, containerization (e.g., Docker), and orchestration tools (e.g., Kubernetes).
  • Solid grasp of software engineering principles including testing, version control (e.g., Git), and security.
  • Familiarity with the Machine Learning Development Lifecycle (MDLC) and best practices for reproducibility and scalability.
  • Strong communication and collaboration skills, with experience working across technical and business teams.
  • Ability to anticipate ambiguity and devise scalable solutions to address it.
  • Experience with Databricks for scalable data and ML workflows.
  • Familiarity with Feature Store concepts and implementation.
  • Exposure to real-time prediction systems and streaming data architectures.
  • Knowledge of data governance, model explainability, and responsible AI practices.
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