Responsibilities
- Design, develop, and deploy end-to-end AI/ML solutions, including data ingestion, feature engineering, model training, deployment, monitoring, and lifecycle management.
- Build scalable, cloud-native AI/ML applications and services using AWS technologies such as SageMaker, ECS, Lambda, S3, EventBridge, and Step Functions.
- Develop and maintain machine learning, data engineering, and MLOps pipelines supporting batch and real-time workloads.
- Design and implement Generative AI solutions leveraging Large Language Models (LLMs), Advanced RAG, vector databases, knowledge retrieval systems, agentic AI frameworks, and fine-tuning techniques.
- Design and utilize knowledge graphs, graph databases, and relationship-based analytics to enhance enterprise intelligence and decision-making.
- Partner with business stakeholders to translate business challenges into scalable analytical and AI-driven solutions.
- Conduct data discovery and exploratory analysis, establish data lineage, and perform root cause analysis to ensure data quality and reliability.
- Implement model monitoring, observability, alerting, and operational support processes for production AI/ML solutions.
- Ensure adherence to enterprise AI governance, security, Responsible AI, privacy, and model risk management standards.
- Serve as a machine learning engineering subject matter expert, lead technical design discussions, and mentor team members on AI/ML best practices.
- Stay current on emerging AI technologies and evaluate their application to business opportunities.
Qualifications
- Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field; Master's degree preferred.
- 6+ years of experience in Machine Learning Engineering, Data Engineering, Software Engineering, or a related discipline.
- 3+ years of hands-on experience building scalable data pipelines and ETL solutions using AWS services.
- Strong proficiency in Python and modern software engineering practices.
- Experience deploying and supporting production-grade AI/ML applications in cloud environments, preferably AWS.
- Strong experience with SageMaker, MLOps, CI/CD pipelines, model deployment, monitoring, and Machine Learning Development Lifecycle (MDLC) practices.
- Experience with containerization and orchestration technologies such as Docker, ECS, and Kubernetes.
- Experience with Generative AI technologies, including LLMs, Advanced RAG, vector databases, semantic search, agentic AI frameworks, and enterprise knowledge retrieval systems.
- Experience designing and implementing knowledge graph solutions and graph databases.
- Strong understanding of software engineering fundamentals, including system design, testing, security, observability, and version control.
- Ability to lead technical initiatives, influence architectural decisions, and collaborate effectively across business and technology teams.
Preferred Experience
- Real-time data processing and streaming technologies such as Kafka, Flink, or Kinesis.
- AI governance, Responsible AI, and model risk management frameworks.
- Enterprise-scale AI platform development and solution architecture.
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.
Vanguard, one of the world's leading investment management companies, serves individual investors, institutions, employer-sponsored retirement plans, and financial professionals. We have a diverse and talented crew with a culture that promotes teamwork, along with an unwavering focus on serving our clients' best interests.
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