AI-ML Architect

Position: AI/ML Enterprise Architect

Location: Newark, NJ

Type Long term Contract

Job Description:

Role Purpose

Define enterprise AI/ML platform patterns and standards, create ML Ops frameworks and templates, establish model governance standards, and provide the patterns that enable consistent, responsible, scalable deployment of AI/ML capabilities. This role focuses on creating ML patterns and standards, not building individual models.

What Makes This Role Unique

GenAI integration architect: Lead the enterprise approach to LLM and GenAI integration with RAG patterns, vector databases, and prompt engineering standards

ML Ops framework creator: Design the ML Ops templates that enable consistent model deployment across the organization

Responsible AI champion: Embed ethics, bias detection, and explainability into ML patterns from the start

Emerging technology: Shape how the organization adopts cutting-edge AI/ML technologies

Key Responsibilities

Enterprise ML Standards & Patterns (40%)

• Define ML platform reference architectures (training, serving, monitoring)
• Create MLOps patterns and templates (ML pipeline templates, CI/CD templates for models, model versioning and registry patterns)
• Establish model governance framework (approval process, versioning standards, lineage tracking, performance monitoring standards)
• Define feature store patterns and feature engineering standards
• Document model deployment patterns (real-time API, batch inference, streaming, embedded)
• Create GenAI/LLM integration patterns (RAG architecture templates, LLM API integration patterns, prompt engineering standards, vector database patterns)
• Establish model monitoring and observability standards (drift detection, performance metrics)

ML Frameworks & Templates (35%)

• Build ML project templates for common use cases (classification, regression, NLP, computer vision)
• Create model serving templates (REST API, batch scoring, streaming inference)
• Define responsible AI framework (bias detection and mitigation patterns, model explainability standards, ethical AI guidelines, model documentation templates)
• Establish data preparation patterns for ML (feature engineering, data labeling, synthetic data)
• Document ML experimentation standards (experiment tracking, hyperparameter tuning)

Roadmap & Coordination (15%)

• Develop AI/ML platform modernization roadmap
• Define GenAI and LLM adoption strategy
• Coordinate with Data Platform team on ML data pipeline patterns
• Evaluate ML platform technologies and provide recommendations

Governance & Enablement (10%)

• Train solution architects and data scientists on ML patterns
• Review ML solution architectures for pattern compliance
• Participate in AI governance and ethics reviews
• Maintain ML pattern catalog

Required Qualifications

Education:

Bachelor’s degree in computer science, Data Science, Machine Learning, or related field

Experience:

• 7 years in machine learning, AI architecture, or data science
• 5 years creating ML platform architectures and MLOps frameworks
• Proven experience deploying ML models at production scale
• Experience with GenAI/LLM integration and RAG architectures
• Track record establishing model governance and responsible AI practices

Certifications (Preferred):

• Cloud ML/AI certification (AWS Machine Learning, Azure AI Engineer, Google Cloud ML Engineer)
• MLOps certification
• TOGAF certification

Preferred Qualifications

• Research publications in ML/AI conferences or journals
• Experience with large-scale ML systems (billions of predictions/day)
• Deep expertise in GenAI and LLM architectures
• Track record implementing responsible AI and model governance at scale
• Experience in regulated industries requiring model explainability