Description

Define and own enterprise AI and GenAI reference architectures, including LLM platforms, RAG patterns, and agentic systems, while leading architecture decisions for model selection and vendor integrations.

This role is on-site.

Responsibilities

• Establish standardized architectural patterns for model serving, prompt management, orchestration, and agent frameworks.
• Architect end-to-end MLOps capabilities, including CI/CD for ML, feature stores, model monitoring, and drift detection.
• Design AI and GenAI solutions using AWS-native services such as Amazon Bedrock, SageMaker, Lambda, ECS/EKS, S3, DynamoDB, Aurora, OpenSearch, IAM, KMS, VPC, and CloudWatch.
• Partner with security and compliance teams to define AI governance, guardrails, and responsible AI principles.
• Mentor architects and senior engineers on AI architecture and MLOps best practices while driving alignment across technical teams.

Required Skills

• 10+ years of experience in Application Development, Systems Engineering, or IT management with a Bachelor's degree.
• Deep expertise in AI/ML architecture, specifically GenAI, LLM-based systems, and RAG patterns.
• Strong experience designing MLOps platforms and enterprise AI foundations.
• Proven experience architecting scalable solutions on AWS.
• Proficiency in Python and SQL.
• Experience with modern data frameworks including Databricks, Airflow, and Snowflake.
• Understanding of CI/CD practices for ML workloads.

Preferred Skills

• Experience with Snowflake Cortex AI and Snowflake Native AI capabilities.
• Proficiency in API design, microservices, and containerization (Docker, Kubernetes).
• Relevant AWS certifications in cloud architecture, AI/ML, or generative AI.

Key Skills

Education

Bachelor's degree