AIML Engineer

Title: AI/ML Engineer

Location: 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 locations

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

• Bachelors degree in Computer Science Engineering or related field (Masters preferred).
• 8 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 SageMaker
• AWS Glue
• AWS Bedrock
• AWS Data Pipelines
• AWS Lambda Functions