Staff AI Platform Engineer – Agent & Retrieval Infrastructure
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Job Description
Staff AI platform engineer building secure Amazon Bedrock agent and retrieval infrastructure. Enabling Bedrock Ocean’s autonomous ocean-data platform and customer intelligence products.
Responsibilities:
- Design the Amazon Bedrock integration, including agent and action group configuration, backend APIs, model access, throughput, and cross-environment deployment
- Own the retrieval pipeline from ingestion and chunking through embedding and storage in Amazon OpenSearch Serverless
- Optimize index design, cost, and capacity
- Adapt ingestion pipelines for internal knowledge, ocean data, and customer platforms
- Address geospatial and large-binary dataset challenges
- Implement security with Bedrock Guardrails, VPC and PrivateLink boundaries, least-privilege IAM, and audit trails
- Define governance mechanisms for approval boundaries and safe autonomous agent actions
- Implement LLMOps and observability for tracing, tool calls, and retrieval performance using CloudWatch and tools such as Langfuse or Phoenix
- Build automated evaluation infrastructure, track results, and manage model-accuracy release gates
- Provide abstraction layers, SDKs, and self-service environments for independent AI feature delivery
- Manage infrastructure as code across environments
- Ensure deployment safety and participate in incident reviews
- Define the sequencing of internal, operational, ocean-data, and customer-facing retrieval capabilities
- Collaborate with the core data transport team on retrieval and data-freshness requirements
Requirements:
- 8+ years in software and infrastructure engineering
- Deep production backend experience; Python or TypeScript preferred, Go acceptable
- Staff-level ownership of technical direction
- Hands-on experience standing up Amazon Bedrock in production, including agents, knowledge bases, guardrails, model access, throughput, and quotas
- Containerized service deployment on ECS, EKS, or Lambda
- Ownership of CI/CD
- Practical RAG and vector search experience, including embeddings, chunking strategies, semantic search quality, and managed vector databases at production scale and cost
- Experience building or substantially extending ingestion pipelines over messy, heterogeneous, unstructured sources
- Strong AWS expertise: IAM, VPC networking, PrivateLink, Lambda, S3, KMS, CloudWatch, and infrastructure as code using Terraform, CDK, or CloudFormation
- Production experience with LLM features or autonomous agents
- Experience securing agentic systems, including tool permissions, prompt injection, exfiltration risk, sensitive data handling, and human-in-the-loop controls
- Experience designing developer-facing APIs, SDKs, or platform services with an API-first mindset
- Experience building and operating multi-tenant services with customer data isolation
- Demonstrated staff-level technical leadership and system design judgment
- Pragmatic preference for managed infrastructure
- Ability to work across several functions on a small team and document systems thoroughly
- Legally authorized to work in the US
- Eligible to obtain a government security clearance if required
Benefits:
- Equity
- Equal opportunity employer
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