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Numentica

AI Application Engineer

Numentica

AI Application Engineer building prototypes and integrations for generative AI on AWS. Collaborating with customer developers and turning designs into working software.

Posted 7/7/2026contractRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates strong software engineering skills in Python, TypeScript/Node.js, or Java, with hands-on experience in integrating generative AI APIs and building enterprise-level integrations. Proficient in AWS services, REST/serverless architecture, and collaborative development practices to ensure knowledge transfer and self-sufficiency for customer teams.

Highest-signal resume keywords
Python ProgrammingAWS Services IntegrationGenerative AI API ExperienceREST/Serverless ArchitectureMCP Server Configuration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Software EngineeringAPI DevelopmentDocument Intake WorkflowsIntegration AutomationKiro Spec-Driven DevelopmentVector DatabasesEmbeddingsSearch/Retrieval ToolingCI/CD Best PracticesInfrastructure-as-Code
Soft Skills
CollaborationKnowledge TransferWritten CommunicationVerbal Communication
Tools & Technologies
Amazon BedrockAmazon TextractStep FunctionsMicrosoft 365ServiceNowJiraGitHubDynatraceWiz
Certifications & Qualifications
AWS Associate-Level Certification
Industry Keywords
Generative AIRAG FeaturesEnterprise IntegrationsRegulated EnvironmentsResponsible-AIPHI-PII Handling

Tech Stack

Tools & technologies
AWSJavaJavaScriptNode.jsPythonServiceNowTypeScript

About the role

Key responsibilities & impact
  • The AI Application Engineer builds the prototypes, services, workflows, and integrations that bring generative AI use cases to life on AWS.
  • This is a hands-on role: the engineer pairs directly with the customer's developers in joint build sessions, turning designs into working software while transferring knowledge so the customer's team becomes self-sufficient.
  • The work spans Bedrock applications and agents, enterprise integrations, MCP-based tooling, and spec-driven development.
  • Build prototypes and pilot-ready services implementing AI use cases on Amazon Bedrock, including RAG and Bedrock Agents.
  • Develop APIs, UI components, and workflow integrations connecting AI capabilities to enterprise applications and data sources.
  • Build document intake, classification, and extraction workflows using services such as Amazon Textract and Step Functions where relevant.
  • Configure and integrate MCP (Model Context Protocol) servers, and work within Kiro spec-driven development workflows, hooks, and steering rules.
  • Implement integrations with the Microsoft 365 ecosystem (SharePoint, Outlook/Exchange, Teams, OneDrive) and delivery/observability tools such as ServiceNow, Jira, GitHub, Dynatrace, or Wiz where approved.
  • Pair with customer developers in joint build sessions, demonstrating implementation techniques and helping them become self-sufficient.
  • Implement guardrails, evaluation tests, and grounding/citation behavior according to the architect's designs.
  • Write clean, tested, documented code following reusable development standards, and contribute reusable templates and playbook material.

Requirements

What you’ll need
  • Strong software engineering skills in a common language such as Python, TypeScript/Node.js, or Java.
  • Hands-on experience integrating with LLM or generative AI APIs (Amazon Bedrock preferred) and building RAG or agent-style features.
  • Working knowledge of core AWS services for compute, storage, APIs, and identity, including security fundamentals beyond the AI stack.
  • Comfort building REST/serverless services, integrations, and automation.
  • Experience integrating with enterprise systems via APIs (e.g., Microsoft 365 / Graph, ServiceNow, Jira, GitHub) and handling authentication and access controls.
  • Ability to work directly with a customer's developers in a collaborative, pairing-oriented way and contribute to knowledge transfer.
  • Good written and verbal communication.
  • Experience with MCP server configuration and with Kiro or other spec-driven development tooling (preferred).
  • Experience with vector databases, embeddings, and search/retrieval tooling (preferred).
  • Familiarity with CI/CD, infrastructure-as-code, and testing best practices on AWS (preferred).
  • Exposure to regulated or public-sector environments and responsible-AI / PHI-PII handling (preferred).
  • AWS associate-level certification (preferred).

Benefits

Comp & perks
  • Remote work options