Artificial Intelligence Engineer

Hello Everyone,

Title

AI/ML Engineer

Number of Openings

2

Location

Malvern, PA (3 days’ on-site required)

Interview Process

• Vendor 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 Spark‑based 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.
• Hands‑on experience with Databricks, including:
• Spark‑based data processing and feature engineering
• Databricks ML/MLflow for experiment tracking and model management
• Integrating Databricks with cloud‑native 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