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.
Responsibilities
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.
Qualifications
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.
Nice to Have
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.
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.
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