AWS Architect

1. AWS Certified Machine Learning Engineer Associate
2. AWS Certified AI Practitioner
3. AWS Certified Machine Learning Specialty (MLS-C01)

Here is the revised JD

• Python (3.11+): Hands-on experience building robust applications and agentic logic.
• Amazon SageMaker Unified Studio (SMUS) & MLOps: Hands-on experience with SMUS to manage the end-to-end ML lifecycle. This includes automating model training, registering models in the centralized registry, and configuring real-time/asynchronous inference endpoints within the SMUS ecosystem.
• Infrastructure as Code (IaC):Production-level expertise using Terraform to provision AWS services, including Bedrock Agents, Knowledge Bases, DynamoDB, Lambda, etc.
• CI/CD & DevOps: Proven track record in building and maintaining automated deployment pipelines for cloud-native applications and MLOps workflows.
• AWS Security & Identity: Deep experience with IAM for fine-grained permissions and secure service communication.
• AWS Bedrock AgentCore : Familiarity with the latest ecosystem, specifically the Agent Runtime, Memory, Identity, and Tool Gateway layers.
• Multi-Agent Orchestration: Experience building or enabling collaborative systems using frameworks like LangGraph, strands, etc.
• Model Context Protocol (MCP): Strong understanding of MCP and modern tool/API integration patterns for AI agents.
• AWS Certification (Optional): Holding one or more AWS Professional-level certifications specifically the AWS Certified AI Practitioner or AWS Certified Machine Learning Specialty is highly preferred and considered a strong plus.
• Continuous Learning: Willingness to learn emerging AI technologies, frameworks, and evolving cloud services.