Job Description - AI/ML Ops Enterprise Architect

Job Title

AI/ML Ops Enterprise Architect

Job Summary

We are seeking an experienced AI/ML Ops Enterprise Architect to lead the design, implementation, and governance of enterprise-scale AI/ML platforms in a cloud-native, multi-tenant SaaS environment. The ideal candidate will possess deep expertise in MLOps, AWS AI/ML services, Kubernetes, DevOps, Data Architecture, Enterprise Integration, and Generative AI.

The architect will collaborate with Data Scientists, Data Engineers, Enterprise Architects, DevOps teams, and Business Stakeholders to define AI platform strategy, build scalable AI infrastructure, establish MLOps best practices, and drive enterprise-wide AI transformation initiatives.

Key Responsibilities

Enterprise AI Architecture

• Design and implement scalable enterprise AI/ML architecture on AWS.
• Build secure, cloud-native AI platforms for multi-tenant SaaS environments.
• Define enterprise AI/ML reference architectures, standards, and governance frameworks.
• Develop AI technology roadmaps aligned with business objectives.

MLOps Platform Engineering

• Design, implement, and optimize enterprise MLOps platforms.
• Standardize model development, deployment, monitoring, and lifecycle management.
• Build automated ML deployment pipelines and model governance processes.
• Support continuous model training, validation, monitoring, and retraining.

AI Infrastructure & Cloud

• Architect scalable AWS AI/ML infrastructure using services such as SageMaker, EKS, S3, Lambda, IAM, CloudWatch, and Redshift.
• Deploy AI workloads on Kubernetes platforms.
• Design highly available and secure cloud-native AI solutions.
• Optimize AI infrastructure performance, scalability, and cost.

Kubernetes & Workflow Orchestration

• Manage Kubernetes clusters supporting AI/ML workloads.
• Design AI workflow orchestration using:
  • Kubeflow
  • Argo Workflows
  • Argo CD
  • Apache Airflow
• Evaluate orchestration platforms and recommend best-fit solutions.

Data Platform Architecture

• Design enterprise data platforms.
• Build Data Mesh architectures and Data Products.
• Design scalable data pipelines and feature engineering workflows.
• Integrate enterprise data sources with AI platforms.

Enterprise Integration

• Integrate AI platforms with enterprise applications including:
  • Guidewire
  • Salesforce
  • Enterprise APIs
  • MuleSoft
• Build scalable API-based AI services.

DevOps & Automation

• Automate AI platform deployment using:
  • Argo CD
  • GitOps
  • CI/CD
  • Infrastructure as Code
• Standardize deployment pipelines and operational processes.

AI & Generative AI

• Build enterprise AI applications using:
  • Large Language Models (LLMs)
  • OpenAI
  • Gemini
  • Agentic AI
  • LangGraph
  • AutoGen
• Design Retrieval-Augmented Generation (RAG) architectures.
• Develop AI automation solutions.

Leadership

• Mentor engineering teams on MLOps best practices.
• Define architecture standards and governance.
• Conduct architecture reviews and technical guidance.
• Collaborate with cross-functional teams and business stakeholders.

Required Skills

Cloud Platforms

• Amazon Web Services (AWS)
• AWS SageMaker
• Amazon EKS
• AWS Lambda
• AWS IAM
• AWS CloudWatch
• Amazon Redshift
• Multi-tenant SaaS Architecture

MLOps

• AWS SageMaker
• Vertex AI
• Databricks ML
• MLflow
• Model Deployment
• Model Monitoring
• Model Governance
• Model Lifecycle Management

Kubernetes & Containers

• Kubernetes
• Amazon EKS
• ROSA
• Docker
• Kubeflow
• Argo Workflows
• Argo CD

Data Engineering

• Apache Airflow
• Apache Beam
• Data Pipelines
• ETL
• Feature Engineering
• Feature Store
• Tecton
• FeatureForm

Data Platforms

• Snowflake
• Amazon Redshift
• BigQuery
• Databricks
• Data Mesh
• Data Products

Enterprise Integration

• Guidewire
• Salesforce
• MuleSoft
• REST APIs
• API Gateway

Generative AI

• OpenAI
• Gemini
• LLMs
• Prompt Engineering
• RAG
• LangGraph
• AutoGen
• Google ADK
• AI Agents

Architecture

• Enterprise Architecture
• Solution Architecture
• Cloud Architecture
• Event-Driven Architecture
• Distributed Systems
• Security Architecture
• Multi-tenant SaaS
• Strategy & Roadmap Development

Preferred Skills

• Azure Machine Learning
• Azure AI Services
• Apache Kafka
• Terraform
• GitHub Actions
• Jenkins
• Observability
• ML Security
• FinOps
• Insurance Domain
• Claims Processing
• Policy Administration
• AI Governance

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• 10+ years of Enterprise Architecture or Solution Architecture experience.
• 5+ years designing AI/ML platforms.
• 4+ years working with MLOps tools such as SageMaker, Vertex AI, or Databricks.
• 3+ years with Kubernetes, Kubeflow, Airflow, and Argo CD.
• Experience designing enterprise cloud-native AI solutions.
• Strong leadership and stakeholder management skills