AI/ML Engineer

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.

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.