Job Role: AWS AI Platform Engineer
Location: Raleigh, NC/Pheonix, AZ
Job Description:
Must Have Technical/Functional Skills
Experience
- 10 15 years of experience in Cloud Engineering, Platform Engineering, or Enterprise Architecture
- 4+ years of experience designing and implementing AI/ML and Generative AI solutions
- 2+ years of hands-on experience building RAG systems and AI Agents
- Experience working in large enterprise or financial services environments is highly preferred
Role Summary
We are seeking a Senior Engineer AWS AI Platform & RAG Integration to serve as the technical bridge between the AWS Cloud Infrastructure team, Enterprise AI Platform team, Security, Networking, Data Engineering, and Application Development teams.
The Engineering Lead will drive the onboarding of AI use cases onto the enterprise AI platform by coordinating cloud infrastructure requirements, designing scalable AI integration patterns, and implementing Generative AI solutions using AWS native AI services.
This role combines technical leadership, solution architecture, hands-on engineering, and cross-functional coordination to accelerate enterprise AI adoption while ensuring scalability, security, governance, and operational excellence.
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
- Coordinate networking, IAM, security, storage, and compute requirements
Cross-Team Leadership
- Act as the primary technical liaison between:
o AWS Cloud Infrastructure teams
o AI Platform teams
o Security and IAM teams
o Networking teams
o Data Engineering teams
o Application Development teams
o Enterprise Architecture teams
- Lead technical workshops and architecture discussions
- Coordinate 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 Technologies: 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
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
- Experience in Banking or Financial Services
- 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
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