AI/ML Engineer

Vanguard; Malvern (Chester County); Hybrid; USD 140,000 - 210,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

Vanguard in Malvern, PA is seeking an experienced Machine Learning Engineer to design, develop, and deploy end-to-end AI/ML solutions across the lifecycle, including data ingestion, feature engineering, training, deployment, and monitoring.

The role emphasizes cloud-native AWS stack (SageMaker, ECS, Lambda, S3), MLOps, knowledge graphs, LLMs, and governance, with a hybrid work model and opportunities to mentor teammates and shape enterprise AI initiatives.

Qualifications

• 6+ years in Machine Learning Engineering or related fields.
• 3+ years building scalable data pipelines with AWS.
• Strong Python and software engineering fundamentals.
• Experience deploying production AI/ML on AWS (SageMaker preferred).
• Experience with containerization (Docker, Kubernetes) and MDLC practices.

Responsibilities

• Design, develop, and deploy end-to-end AI/ML solutions across the lifecycle.
• Build scalable cloud-native AI/ML apps using AWS technologies.
• Develop ML, data, and MLOps pipelines for batch and real-time workloads.
• Design and implement Generative AI solutions with LLMs and RAG.
• Utilize knowledge graphs and graph databases to boost enterprise analytics.
• Partner with stakeholders to translate business problems into analytical AI solutions.
• Perform data discovery, lineage, and root-cause analysis for data quality.
• Implement model monitoring, observability, and production support.
• Ensure governance, security, privacy, and model risk standards.
• Lead design discussions and mentor engineers in AI/ML best practices.
• Stay current with emerging AI tech and assess business potential.

Skills

Machine Learning Engineering

Data Engineering

Python

AWS SageMaker

MLOps

CI/CD

Cloud architecture

Docker

Kubernetes

LLMs

RAG

Knowledge graphs

System design

Team leadership

Education

Bachelor's degree in related technical field

Master's degree preferred

Tools

Docker

AWS ECS

Kubernetes

SageMaker

Lambda

S3

EventBridge

Step Functions

Kafka

Flink

Kinesis

Knowledge graph databases

Job description

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.

Sponsorship

Vanguard is not offering visa sponsorship for this position.

About Vanguard

At Vanguard, we don't just have a mission—we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

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.