Senior AI Implementation Engineer

Responsibilities

• Design, develop, test, and deploy AI applications using Amazon Bedrock foundation models and AWS services.
• Build retrieval-augmented generation (RAG) solutions using Amazon Bedrock Knowledge Bases or custom retrieval pipelines, including ingestion, chunking, embeddings, vector search, and source citations.
• Implement AI agents and tool integrations that connect models to enterprise APIs and workflows, with explicit permissions, validation, and human approval where needed.
• Develop prompts and structured extraction workflows for document understanding, classification, summarization, and business process automation.
• Build Python services and APIs that integrate AI capabilities with customer applications, databases, and enterprise data sources.
• Create representative evaluation datasets and automated tests to measure accuracy, grounding, extraction quality, and agent task completion; validate changes before release.
• Implement layered AI safeguards using Amazon Bedrock Guardrails, IAM, encryption, data access controls, output validation, and protections against prompt injection.
• Automate infrastructure and application delivery using infrastructure as code, Git, CI/CD, versioned prompts and configurations, and repeatable deployments.
• Monitor and troubleshoot production AI workloads; optimize model selection, token usage, latency, reliability, and cost, with retry and fallback strategies.
• Translate customer requirements into solution designs and implementation plans; deliver demonstrations, document decisions, and support operational handoff.

Skills you bring with you

• Strong Python and software engineering skills, including API development, automated testing, debugging, code review, and maintainable application design.
• Hands-on experience implementing applications with Amazon Bedrock, including model invocation, prompt design, inference settings, and integration through AWS SDKs.
• Practical experience with RAG, embeddings, vector stores, retrieval quality, and grounding responses in enterprise data, including Amazon Bedrock Knowledge Bases.
• Experience building agent workflows and tool calling, with schema validation, controlled execution, error handling, and integration with external systems.
• Experience with supporting AWS services such as Lambda, S3, API Gateway, Step Functions, SQS, and CloudWatch, plus SQL and data integration fundamentals.
• Understanding of AI evaluation, hallucination risks, security, and data privacy; hands-on experience with IAM and infrastructure as code using Terraform, AWS CDK, or CloudFormation.

Qualifications

• BS or MS in Computer Science, Information Systems, Engineering, or a related field preferred; equivalent experience considered.
• 5+ years of experience in software engineering, cloud application development, data engineering, or related roles, with hands-on responsibility for production systems.
• Demonstrated experience delivering generative AI applications from prototype to production, including hands-on implementation using Amazon Bedrock and AWS.

Additional experience we value

• Amazon Bedrock Agents and production agent orchestration, including tool integration, state management, observability, and controlled access to enterprise systems.
• Intelligent document processing using Amazon Bedrock Data Automation, Amazon Textract, or model-based extraction, with normalization, validation, and source traceability.
• Vector search and retrieval implementations using Amazon OpenSearch or PostgreSQL with pgvector, including metadata filtering and retrieval tuning.
• AI delivery practices such as evaluation pipelines, prompt and model versioning, regression testing, staged releases, rollback, and production quality monitoring.
• Effective use of AI coding assistants, with independent validation of generated code, automated tests, and adherence to customer security and development standards.
• Consulting and technical leadership skills, including customer discovery, engineering mentorship, and clear communication of quality, security, performance, and cost tradeoffs.

Travel required

• Travel typically 5% or less a year