We are seeking a Machine Learning Engineer to join our AI/ML Engineering team. You will be responsible for developing and optimizing complex data pipelines, integrating model pipelines, and building scalable AI/ML solutions, including large language models (LLMs). The ideal candidate will possess a robust background in traditional machine learning, deep learning, and significant experience with large datasets and cloud-based AI services. 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 5 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 pipelines. 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.