AI-ML Enterprise Architect : Newark, NJ : Only local
Deepika Dua
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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
Regards,
Deepika Dua
VBeyond Corporation
https://www.linkedin.com/in/deepika-dua-018459166/
E: Deep...@vbeyond.com | www.vbeyond.com
390 Amwell Road, Suite # 107, Hillsborough, NJ 08844
Note – VBeyond is fully committed to Diversity and Equal Employment Opportunity.
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