AWS AI Platform Engineer

Key Responsibilities

• AI Platform Integration Lead onboarding of business applications onto the enterprise AI platform
• Translate business and AI requirements into AWS infrastructure and platform capabilities
• Design reusable AI integration patterns and reference architectures
• Define enterprise standards for AI application integration
• Support multiple AI initiatives across business domains
• RAG and Agentic AI Development
• Design and implement Retrieval-Augmented Generation (RAG) architectures
• Build AI agents and multi-agent workflows for enterprise use cases
• Design enterprise knowledge retrieval and semantic search solutions
• Develop reusable AI orchestration components and AI APIs
• Integrate enterprise data sources into AI knowledge bases
• Implement prompt engineering and context management strategies
• AWS Cloud Platform Engineering
• Work with AWS Cloud Infrastructure teams to use AI to provision and configure AWS Cloud infrastructure
• Design cloud-native AI architectures using AWS managed services
• Support infrastructure automation and deployment pipelines
• Ensure high availability, scalability, and resilience of AI workloads
• Cross-Team Leadership
• Act as the primary technical liaison between:
• AWS Cloud Infrastructure teams
• AI Platform teams
• Security and IAM teams
• Networking teams
• Data Engineering teams
• Application Development teams
• Enterprise Architecture teams
• Lead technical workshops and architecture discussions
• Cross-functional delivery activities
• Mentor engineering teams adopting AI capabilities
• AI Governance and Operational Excellence
• Ensure AI solutions comply with enterprise security and governance standards
• Design secure AI integration patterns
• Implement AI guardrails and Responsible AI controls
• Support AI evaluation, monitoring, and observability
• Drive AI platform best practices and reusable accelerators

Required Technical Skills

• AWS Cloud: VPC, IAM, EC2, ECS, EKS, Lambda, S3, API Gateway, CloudWatch, CloudFormation, EventBridge, SNS/SQS, Step Functions, KMS, Secrets Manager, Terraform, Elasticsearch, Cost Analysis, Budgeting
• AWS AI Services: Amazon Bedrock, SageMaker AI, Amazon Knowledge Bases, Amazon OpenSearch, Amazon Titan, Bedrock Agents, Bedrock Guardrails, Textract, Comprehend, Transcribe, Rekognition, Neptune AI
• Techologies: RAG architecture, Vector databases, Embeddings, Vector Search, Sematic search, Prompt engineering, Context Engineering, Agentic AI, Multi-agent orchestration, MCP, LangChain, LangGraph, LlamaIndex, AI evaluation techniques, Hallucination Mitigation Techniques, AI governance, LLM Models (Anthropic)
• Programming: Python, Java, REST APIs, SDK integration, Git, CI/CD, Claude Code
• Code Data Skills: SQL, NoSQL, Document processing, Data chunking, Metadata management, Data ingestion pipelines

Leadership Skills

• Executive communication
• Cross-functional coordination
• Technical leadership
• Architecture governance
• Stakeholder management

Preferred Qualifications

• Experience with enterprise AI platform implementation
• Familiarity with Responsible AI and AI Governance frameworks
• Experience implementing secure AI solutions in regulated environments
• AWS Professional or Specialty Certifications
• Experience with DevSecOps and Platform Engineering practices

Salary Range

$110,000 - $130,000 per year