Role Overview:This role is for an AWS AI Platform Engineer responsible for leading the integration of business applications onto an enterprise AI platform. The engineer will translate business and AI requirements into AWS infrastructure and platform capabilities, design reusable AI integration patterns, and define enterprise standards for AI application integration, supporting multiple AI initiatives across various business domains.
Key Responsibilities:- 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.
- 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.
- 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.
- Act as the primary technical liaison between AWS Cloud Infrastructure, AI Platform, Security, IAM, Networking, Data Engineering, Application Development, and Enterprise Architecture teams.
- Lead technical workshops and architecture discussions.
- Coordinate cross-functional delivery activities.
- Mentor engineering teams adopting AI capabilities.
- 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 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, Semantic 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.
Qualifications:- No specific years of experience mentioned.
Preferred Skills:- 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.
