AI/ML Engineer

Title: AI/ML Engineer

Number of Openings: 2

Location: Malvern, PA (3 days on-site required)

Interview Process

• 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.
• Agentic Cloud Deployment & Integration: Design and deploy agentic AI services in cloud environments, integrating models, tools, APIs, and data sources to deliver scalable, autonomous workflows.
• Databricks & Lakehouse Engineering: Develop and optimize ML and GenAI workloads using Databricks, including Sparkbased data pipelines, feature engineering, and model training/inference on the Lakehouse platform.
• Unity Catalog & Governance: Implement Unity Catalog for centralized data, model, and feature governance, ensuring secure access control, lineage tracking, and compliance across ML and GenAI assets.
• 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.
• Handson experience with Databricks, including:
  • Sparkbased data processing and feature engineering
  • Databricks ML/MLflow for experiment tracking and model management
  • Integrating Databricks with cloudnative ML services
• Experience implementing Unity Catalog for centralized governance of data, features, and models, including access controls, lineage, and auditability.
• 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 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 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.