AI/ML Engineer

Please go through the JD,Position: AI/ML EngineerLocation: Malvern, PADuration: This will be a long-term contract; multi-year most likely.Client: The Vanguard Group, Inc. - PhiladelphiaVisa Restrictions: NoneSub Vending: NoPay Rate: $58/Hr on W2 without benefitsBill Rate: $80Job DescriptionEXCLUSIVE OPENING!!!Client: VanguardTitle: AI/ML EngineerNumber of Openings: 2Location: Malvern, PA (1 st Choice); Charlotte, NC (2 nd Choice); Dallas, TX 75248 (3 rd Choice)3 days' on-site required in one of these 3 locationsInterview ProcessApex Systems Technical Screening (1 hour MS Teams Video)1 hour MS Teams Video I/V with client teamCore ResponsibilitiesAgentic 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.ResponsibilitiesDesign, 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.QualificationsBachelor'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:o Designing and deploying production-grade ML systems.o Building scalable data pipelines and ML workflows.o 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 SageMakerAWS GlueAWS BedrockAWS Data PipelinesAWS Lambda FunctionsExperience with Generative AI model development builing 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 evaluationExperience 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 HaveKnowledge of data governance, model explainability, and responsible AI practices.