Remote
The Enterprise Architect – Full Stack, AI/ML will be responsible for defining and leading enterprise grade solution architectures that integrate modern full stack engineering practices with scalable AI/ML capabilities. The role requires deep experience across application engineering, MLOps, cloud-native architectures, data engineering, and enterprise integration. You will work closely with business, product, engineering, and data science teams to conceptualize, architect, and deliver complex, secure, scalable, and high performing digital ecosystems powered by AI/ML. This role requires 12+ years of hands-on and architectural experience in large-scale enterprise environments.
Key Responsibilities:
1. Enterprise Architecture & Strategy Define end to end architecture for full stack and AI/ML systems 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 initiatives aligned with business goals. Evaluate emerging technologies (AI/ML, DevOps, MLOps, cloud platforms) to accelerate enterprise innovation.
2. AI/ML Solution Architecture Architect scalable ML pipelines, automated workflows, CI/CD & MLOps frameworks (data ingestion feature engineering model development evaluation deployment monitoring). Work with data scientists, ML engineers, and analytics engineers to operationalize AI/ML models at scale. Ensure governance, compliance, versioning, reproducibility, and monitoring for AI/ML systems.
3. Full Stack & Platform Architecture Design and review enterprise applications combining backend, frontend, cloud APIs, and microservices. Lead architecture for full stack development teams including scalable frontend, middleware, data APIs, microservices, and cloud-native services. Guide solution teams on performance optimization, caching, distributed systems, and containerized deployments (Docker/K8s). Oversee API-first integration patterns, event-driven designs, and asynchronous architectures.
4. Cloud & DevOps/MLOps Integration Architect multi cloud and hybrid solutions across AWS, Azure, and GCP ensuring interoperability and vendor neutral design. (Patterns similar to Carelon Apigee Architect content on multi cloud design). [Carelon Ap…Architect | Word] Lead cloud automation, DevOps pipelines, infrastructure as code, and observability tooling. Implement secure, robust API management and enterprise connectivity models.
5. Stakeholder Leadership & Governance Partner with business leaders to translate complex business goals into technical roadmaps. Mentor engineering teams and foster best practices in solution delivery, coding, security, scalability, and architecture rigor. Facilitate architecture review boards, technical audits, and solution governance.
Required Skills & Experience Technical Expertise Full Stack Engineering: Strong background in Java, Node.js, Python, Angular/React, REST APIs, microservices, event-driven architecture. (Aligned with similar roles like Java Architect / Full Stack Developer).
AI/ML Engineering: Experience with ML frameworks (TensorFlow, scikit learn, MLlib), feature engineering, pipeline automation, monitoring, and operational ML.
Cloud & Platform Architecture: Deep knowledge of AWS/Azure/GCP, cloud networking models, containers (Docker), Kubernetes, serverless, and distributed systems.
Data Engineering: Strong understanding of data warehousing concepts, ETL frameworks, governance, metadata management, and real time data architectures. DevOps/MLOps: CI/CD (Azure DevOps, GitHub, Jenkins), model deployment automation, IaC (Terraform/CloudFormation). Soft Skills Excellent leadership, problem solving, and communication skills fit for enterprise architect roles. Ability to drive cross functional initiatives and influence without authority. Strong stakeholder management, negotiation, and architectural storytelling abilities.
Experience 12+ years of IT experience, including: 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, multi disciplinary teams.
Preferred Qualifications Certifications: TOGAF, cloud architect certifications, AI/ML specialization. Experience with enterprise-scale MLOps & federated data science operations Background in regulated industries (finance, healthcare, telecom) is a plus.
