Hiring | Enterprise Architect - AI/ML , Fullstack | Remote
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Enterprise Architect – Full Stack, AI/ML
Tax Work Location: USA – Working from Home
Role Overview
The Enterprise Architect – Full Stack, AI/ML will define and lead enterprise-grade solution architectures integrating modern full-stack engineering practices with scalable AI/ML capabilities.
This role requires deep expertise across application engineering, MLOps, cloud-native architectures, data engineering, and enterprise integration. The architect will collaborate with business, product, engineering, and data science teams to design and deliver secure, scalable, and high-performing AI-powered digital ecosystems.
Experience Required: 12+ years in large-scale enterprise environments.
Key Responsibilities
1. Enterprise Architecture & Strategy
Define end-to-end architecture across discovery, data management, model development, deployment, and operations.
Establish enterprise architecture principles, standards, and governance models for AI-enabled platforms.
Drive digital modernization and cloud transformation aligned with business objectives.
Evaluate emerging technologies (AI/ML, DevOps, MLOps, cloud platforms).
2. AI/ML Solution Architecture
Architect scalable ML pipelines and automated workflows.
Design CI/CD and MLOps frameworks including:
Data ingestion
Feature engineering
Model development & evaluation
Deployment & monitoring
Operationalize AI/ML models at enterprise scale.
Ensure governance, compliance, reproducibility, and monitoring of ML systems.
3. Full Stack & Platform Architecture
Design enterprise applications across backend, frontend, APIs, and microservices.
Lead architecture for scalable frontend, middleware, data APIs, and cloud-native services.
Guide performance optimization, distributed systems, caching, and containerized deployments (Docker/Kubernetes).
Implement API-first, event-driven, and asynchronous architectures.
4. Cloud & DevOps/MLOps Integration
Architect multi-cloud and hybrid solutions across AWS, Azure, and GCP.
Ensure interoperability and vendor-neutral design.
Lead cloud automation, DevOps pipelines, and Infrastructure as Code.
Implement secure API management and enterprise connectivity models.
Establish observability and monitoring frameworks.
5. Stakeholder Leadership & Governance
Translate business goals into technical roadmaps.
Mentor engineering teams on best practices in architecture, coding, security, and scalability.
Facilitate architecture review boards and governance forums.
Conduct technical audits and solution reviews.
Required Skills & Experience
Technical Expertise
Full Stack Engineering
Java, Node.js, Python
Angular and/or React
REST APIs
Microservices architecture
Event-driven systems
AI/ML Engineering
ML frameworks (TensorFlow, scikit-learn, MLlib)
Feature engineering
Pipeline automation
Operational ML & monitoring
Cloud & Platform Architecture
AWS, Azure, GCP
Cloud networking models
Containers (Docker)
Kubernetes
Serverless architectures
Distributed systems
Data Engineering
Data warehousing concepts
ETL frameworks
Governance & metadata management
Real-time data architectures
DevOps / MLOps
CI/CD (Azure DevOps, GitHub, Jenkins)
Model deployment automation
Infrastructure as Code (Terraform, CloudFormation)
Soft Skills
Strong leadership and communication skills
Enterprise-level architectural thinking
Ability to influence cross-functional stakeholders
Strong problem-solving and negotiation capabilities
Experience leading global, multi-disciplinary teams
Experience Requirements
12+ years of overall IT experience
5–7+ years in solution or enterprise architecture roles
Demonstrated delivery of large-scale enterprise platforms with AI/ML components
Experience leading global, cross-functional teams
Preferred Qualifications
TOGAF certification
Cloud architect certifications (AWS/Azure/GCP)
AI/ML specialization certifications
Experience with enterprise-scale MLOps & federated data science
Background in regulated industries (Finance, Healthcare, Telecom)