AI/ML Engineer

AI/ML Engineer

Position: AI/ML Engineer

Location: Malvern, PA (1st Choice); Charlotte, NC (2nd Choice); Dallas, TX 75248 (3rd Choice)

Duration: This will be a long-term contract; multi-year most likely.

Client: The Vanguard Group, Inc. - Philadelphia

Visa Restrictions: None

Sub Vending: No

Pay Rate: $58/Hr on W2 without benefits

Bill Rate: $80

Job Description

EXCLUSIVE OPENING!!!

Vanguard

Number of Openings: 2

3 days' on-site required in one of these 3 locations

Interview Process:

• Apex Systems Technical Screening (1 hour MS Teams Video)
• 1 hour MS Teams Video I/V with client team

Core Responsibilities

• Agentic AI & MCP Integration: Implement agentic frameworks (e.g., LangGraph, AutoGen) and Model Context Protocol (MCP) for secure tool orchestration.
• Generative AI Development: Build LLM-based applications with RAG, structured output, and evaluation frameworks.
• AWS ML Engineering: Deploy models using SageMaker pipelines, ECS/ECR, Lambda; manage CI/CD and monitoring.
• Security & Identity: Integrate Okta/JWT token for API and service authentication; enforce token validation and claims.
• Governance: Deliver artifacts required by MDLC/MPLC (Model Documents, Data Dictionary, Monitoring Plan).
• Collaboration: Partner with PO, and business stakeholders to align solutions with objectives.

Responsibilities

• Design, develop, and optimize complex data pipelines using machine learning engineering best practices to ensure scalability, efficiency, and reliability.
• Develop and implement robust MLOps pipeline to support the deployment, monitoring, and lifecycle management of AI/ML models in production environments.
• Integrate and maintain data and model pipelines, proactively diagnosing data quality issues and documenting assumptions.
• Collaborate closely with data scientists to validate model-ready datasets and ensure thorough, accurate feature documentation.
• Conduct exploratory data analysis and discovery on raw data sources, incorporating business context to support model development.
• Track data lineage and perform root cause analysis during early-stage exploration or issue resolution.
• Partner with internal stakeholders to understand business processes and translate them into scalable analytical solutions.
• Develop and maintain model monitoring scripts, investigate alerts, and coordinate timely resolutions.
• Act as a subject matter expert in machine learning engineering on cross-functional teams, contributing to high-impact initiatives.
• Stay current with advancements in AI/ML and evaluate their applicability to business challenges.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field (Master's preferred).
• 6+ years of experience across Artificial Intelligence (AI) / Machine Learning (ML) engineering, data engineering, and MLOps implementation, including:
• Designing and deploying production-grade ML systems.
• Building scalable data pipelines and ML workflows.
• Managing model lifecycle in cloud environments.
• Proficient in Python and familiar with ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Strong understanding and experience in AWS Machine Learning Stack including:
• AWS SageMaker
• AWS Glue
• AWS Bedrock
• AWS Data Pipelines
• AWS Lambda Functions
• Experience with Generative AI model development building LLM based applications with RAG.
• Experience implementing agentic frameworks (e.g., LangGraph, AutoGen) and Model Context Protocol (MCP) for orchestration.
• Knowledge of React UI, GraphDB, and GenAI model performance evaluation
• Experience with CI/CD, containerization (e.g., Docker), and orchestration tools (e.g., Kubernetes).
• Solid grasp of software engineering principles including testing, version control (e.g., Git), and security.
• Familiarity with the Machine Learning Development Lifecycle (MDLC) and best practices for reproducibility and scalability.
• Strong communication and collaboration skills, with experience working across technical and business teams.
• Ability to anticipate ambiguity and devise scalable solutions to address it.

Nice to Have

• Knowledge of data governance, model explainability, and responsible AI practices.