AI/ML Architect (AWS MLOps)

TCS • Culver City, CA

2d

About The Position

Architect and implement scalable AWS ML/AI cloud infrastructure in a multi-tenant SaaS environment. Collaborate with data scientists, data engineers, and IT teams to define requirements and best practices for ML model development, deployment, and monitoring. Evaluate and recommend tools, platforms, and cloud technologies for ML Ops, ensuring alignment with enterprise architecture standards. Oversee the integration of ML pipelines with existing enterprise data and application architectures. Familiarity with Guidewire integrations is highly desirable. Oversee ML/AI related Kubernetes cluster management and provide guidance on alternative ML/AI workflow orchestration options such as Argo vs Kubeflow, and ML/AI data pipeline creation, management and governance with tools like Airflow. Employ tools like Argo CD to automate infrastructure deployment and management. Mentor and guide technical teams on ML Ops architecture, tooling, and best practices.

Requirements

• Minimum ten years experience across architecture disciplines with significant enterprise architecture leadership experience required.
• 5+ years: AI/ML Strategy & Roadmap Development.
• 4+ years: MLOps Tools (Eg. AWS Sagemaker, GCP Vertex AI, Databricks).
• 3+ years: ML & Data Pipeline Orchestration (Eg. Kubeflow, Apache Airflow).
• 2+ years: ML Feature Store Tools (Eg. Tecton, Databricks, FeatureForm).
• 3+ years: DevOps (Eg. Argo CD / Argo Workflows), Containerization (Kubernetes,ROSA).
• 3+ years: Enterprise Application Integration (Eg. Guidewire, Salesforce).
• 4+ years: Data Platforms (Eg. Snowflake, RedShift, BigQuery).
• 2+ years: GenAI Tools / LLMs (Eg. OpenAI, Gemini, etc.).
• 1+ year: Agentic AI Frameworks (Eg. LangGraph, Autogen, Google ADK).
• 3+ years: API Orchestration (Eg. Mulesoft, Google Cloud API).
• 3+ years: Data Mesh Architecture & Data Product Design.
• 3+ years: Event-Driven Architecture (EDA).
• 4+ years: Scalable AWS ML/AI Cloud Infrastructure (Multi-tenant SaaS).
• 3+ years: Data Architecture Guidelines Development.
• 3+ years: Security in Distributed Systems.
• 4+ years: Designing Scalable, Decoupled Systems.
• 5+ years: Strategy & Roadmap Creation.
• 3+ years: Influencing with Data-Driven Insights.

Nice To Haves

• Familiarity with Guidewire integrations is highly desirable.
• 4+ years: Functional Knowledge of Insurance Domains (Policy, Claims, Services Ops) - Preferred.
• 2+ years: Legal & Compliance Regulations in Insurance - Preferred.
• 3+ years: Data Product Development for Functional Domains.
• 2+ years: AI-Driven Business Process Automation.

Responsibilities

• Architect and implement scalable AWS ML/AI cloud infrastructure in a multi-tenant SaaS environment.
• Collaborate with data scientists, data engineers, and IT teams to define requirements and best practices for ML model development, deployment, and monitoring.
• Evaluate and recommend tools, platforms, and cloud technologies for ML Ops, ensuring alignment with enterprise architecture standards.
• Oversee the integration of ML pipelines with existing enterprise data and application architectures.
• Oversee ML/AI related Kubernetes cluster management and provide guidance on alternative ML/AI workflow orchestration options such as Argo vs Kubeflow, and ML/AI data pipeline creation, management and governance with tools like Airflow.
• Employ tools like Argo CD to automate infrastructure deployment and management.
• Mentor and guide technical teams on ML Ops architecture, tooling, and best practices.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed