AI Technical Engineer / Architect

Experience building a product from scratch, as they will have access to companies "sandbox" to create this. Looking for a rockstar engineer/architect who can scale these prototypes.

Tech stack - AWS, AWS Bedrock, Claude Code, Lovable.dev, Snowflake

Thinks of the AI Strategist as someone who can come in and understand the business and the Lead is someone who takes the idea and Architects a product.

THE ROLE

The Technical Lead owns the full-stack build of 1 to 2 Springboard pods. You take use case requirements from the AI Strategist, architect the solution, and deliver a working AI prototype inside the sandbox using AI development tooling: LLM APIs, AI-native application builders, workflow orchestration platforms, and cloud-native services. You own every layer of the stack: data ingestion and transformation, AI pipeline design, back-end logic, front-end interface, and enterprise system integration.

This is a delivery role with a fixed endpoint. The prototype is your commitment. You join the opportunity mapping session in Week 1, then scope, build, and iterate through Weeks 2 to 6 based on AI Strategist and executive feedback, manage tradeoffs within the sprint, and hand off complete, documented work that an engineering team can continue without you. You are solely accountable for what ships.

You are also communicating directly with a non-technical executive partner throughout the sprint. The ability to translate architectural decisions into business language, scope ambiguous requirements in real time, and build prototypes that tell a story as clearly as they function is as important as the technical execution.WHAT YOU WILL DO

• Architect and build one to two full-stack AI prototypes per cohort inside the sandbox (AWS), selecting and configuring the appropriate AI development stack for the use case.
• Translate use case requirements and executive input into a scoped technical architecture, making deliberate build-vs-configure-vs-integrate decisions that are achievable within the sprint.
• Design and implement AI pipelines using available LLM APIs (e.g., Claude, OpenAI, Bedrock), including prompt engineering, tool use and function calling, RAG architectures, and agentic workflow patterns as appropriate to the use case.
• Build functional front-end interfaces using AI-native rapid development platforms (e.g., Lovable, Bolt, v0) and wire back-end logic and data flows to deliver a complete, demo-ready application.
• Implement workflow automation and system integration using orchestration platforms (e.g., n8n, Make, Zapier) to connect enterprise data sources, APIs, and downstream systems.
• Develop prototypes that are stable, clearly scoped, and capable of running live in front of executive audiences without failure.
• Iterate rapidly based on AI Strategist and executive feedback throughout Weeks 2 through 6, treating their input as product requirements.
• Assess and articulate the production gap: what was built in the sandbox versus what a production deployment requires across integration, data governance, security, and scale.
• Contribute technical input to the build-vs-buy recommendation, Product Requirements Document, and AIRB submission materials.
• Document architecture decisions, tool selections, API configurations, and implementation details in the Handoff Package so engineering teams can continue the work after Demo Day.
• Attend the weekly AI Strategist sync meeting to stay aligned with the pod, program team, and any shifts in use case direction.

TECHNICAL REQUIREMENTS

• Full-stack AI development proficiency: own the complete solution across data, AI pipeline, API, and UI layers without requiring additional engineering support.
• LLM integration and orchestration: hands-on experience building production-grade AI pipelines including tool use and function calling, retrieval-augmented generation (RAG), structured output handling, streaming, and multi-step agentic workflows.
• AI-native application development: proficiency with rapid development platforms (e.g., Lovable, Bolt, v0) for fast front-end prototyping and end-to-end application assembly.
• Workflow automation and integration: experience with event-driven orchestration and low-code/no-code integration platforms (e.g., n8n, Make, Zapier) for connecting enterprise systems, transforming data, and building automated pipelines without custom middleware.
• Cloud infrastructure: proficient deploying and operating services in AWS sandbox environments including compute, managed AI services, object storage, and serverless functions (e.g., Lambda, API Gateway, Bedrock).
• API and systems integration: strong REST and webhook fluency, OAuth and API key authentication patterns, and JSON/data transformation for connecting to enterprise systems.
• Ability to evaluate and adopt new AI tooling rapidly; this stack evolves and you are expected to keep pace.

WHAT YOU BRING

• A track record of shipping full-stack AI applications under time pressure; working systems with clean demos, not proofs of concept that require significant caveats.
• Strong architectural judgment: you know when to use an LLM API as the core intelligence layer, when to use workflow orchestration, when to use a rapid app builder, and when to write custom code.
• Ability to receive ambiguous or evolving requirements from a non-technical executive and produce specific, buildable scope within the same conversation.
• Clear technical communication; you can explain system architecture, data flows, and AI limitations to non-technical stakeholders without losing precision.
• A collaborative working style; the prototype only succeeds if the Springboarder understands it well enough to own it after Demo Day.

WHAT SUCCESS LOOKS LIKE

• The prototype runs cleanly on Demo Day; you have tested every failure mode and there are no surprises.
• The Springboarder can explain what the prototype does and why it was built the way it was; they are not reading a script you handed them.
• The technical feasibility section of the presentation is honest, specific, and grounded in what you actually built.
• The Handoff Package is thorough enough that a GTS engineer who was not in the room can pick up the work.
• You deliver a complete, functional prototype within the sprint.