AI/ML Engineer

Vanguard Group, Inc.

$110K — $130K *
Enterprise Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in a technical field; Master's preferred.
  • 6+ years in Machine Learning Engineering or related discipline.
  • 3+ years of hands-on experience with AWS data solutions.
  • Strong proficiency in Python and software engineering practices.
  • Experience deploying production-grade AI applications in cloud environments, especially AWS.
  • Expertise in SageMaker, MLOps, and CI/CD pipelines.
  • Knowledge of Generative AI technologies and knowledge graph solutions.

Responsibilities

  • Design, develop, and deploy end-to-end AI/ML solutions.
  • Build cloud-native AI/ML applications using AWS services.
  • Develop and maintain data engineering and MLOps pipelines.
  • Implement Generative AI solutions leveraging Large Language Models.
  • Utilize knowledge graphs and graph databases for decision-making.
  • Translate business challenges into AI-driven solutions in partnership with stakeholders.
  • Conduct data discovery, analysis, and ensure data quality.

Benefits

  • Hybrid working model for flexibility.
  • Collaborative and mission-driven culture.
  • Opportunities for professional development and mentorship.
Full Job Description
At Vanguard's Corporate Services division, we are seeking a Machine Learning/ AI Engineer to design, build, and scale enterprise AI/ML solutions that drive business innovation and support the development, deployment, and operationalization of intelligent applications.

The ideal candidate combines strong software engineering, machine learning, and cloud expertise with hands-on experience delivering production-grade AI solutions. This role will partner closely with business stakeholders, product teams, and engineers to solve complex business problems using machine learning, Generative AI, and advanced analytics.

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.

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