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