AI and Automation Engineer

Join Parachute•Austin, TX

About The Position

Powered by technology and compassionate design, Parachute has reimagined the plasma donation experience into one that is easier and friendlier. Using a simple app, our members can book donations and track earnings from the palm of their hand. In using a tech-forward approach, we’re able to offer each member a highly personable and best-in-class experience that’s consistent at each and every visit. Our vision is to introduce an elevated plasma donation experience to markets with smaller populations that’s grounded in convenience. This model allows us to positively impact the industry supply chain and help patients gain access to the life changing medication they need. We have grown over 50 donation centers in 5 years and are looking for new team members to help us scale to 100 centers. We are searching for individuals who share in our passion for helping others and are invigorated by the speed at which our start-up moves. Parachute is shifting into an AI-forward way of working. The AI Workflow Engineer will sit on the Data team and help build the automations, internal tools, and agent-driven workflows that save time, reduce operational risk, and help teams across the company adopt AI effectively. This role is for a high-velocity Ai power user ie. you live at the edge whether its with Claude Code, Codex, or the latest tooling that pushes the boundaries of what is possible. You move quickly by spinning up multiple agents, delegating work effectively, and then applying strong engineering judgment to review outputs, validate quality, and ship production-ready solutions.

Requirements

Highly proficient in Claude Code and agent-driven development: orchestrate multiple agents, maintain quality through rigorous review, and ship fast.
Fast builder with good taste: you know how to get to a working version quickly without creating a mess.
Strong reviewer: you can read code critically, spot edge cases, and push work over the finish line with high standards.
Comfortable with ambiguity: you can take a messy workflow and turn it into a clear, deliverable project.
Pragmatic about reliability: you validate outputs, build checks, and understand that “trust, but verify” is required with AI.
Strong collaborator: you can work across teams, ask good questions, and translate needs into solutions.
Demonstrated ability to move fast with Claude Code using multi-agent workflows and tight feedback loops.
Python and SQL proficiency.
Experience building integrations using APIs
Familiarity with MCP (Model Context Protocol) and using MCP servers/tools to connect agents to internal systems and data safely.
Comfort working across Azure and/or AWS (shipping workflows, managing basic cloud concepts like permissions/secrets, and deploying lightweight services/jobs).

Nice To Haves

Experience with Snowflake, dbt, Airbyte, and modern ELT patterns.
Experience building lightweight internal tools (simple web apps or Streamlit, Slack/Teams bots, scheduled jobs).
Exposure to Azure and/or AWS (deploying services, permissions/roles, secrets, basic infra).
Experience building guardrails: evaluations, monitoring, logging, human-in-the-loop review, QA harnesses.

Responsibilities

Create agentic processes and automations for recurring business tasks (e.g., summarizing and organizing updates, reconciling inputs across systems, drafting structured analyses, generating follow-ups, identifying gaps).
Integrate with business systems and data products via APIs and MCPs
Prototype and ship internal apps/tools that help teams evaluate new ideas, track outcomes, and learn from past activity.
Run agent-driven development loops: spin up multiple agents to explore approaches, generate code, draft documentation/tests, and accelerate execution—then review PRs and validate correctness, security, and maintainability before merging.
Raise the reliability bar for AI-enabled work by building: automated checks, evaluation harnesses, and regression tests human-in-the-loop review where needed monitoring/alerting for critical workflows
Partner with subject matter experts (e.g. quality/compliance, supply chain, operations) to map workflows, identify failure points, and translate them into automated, testable systems.
Operationalize solutions in our stack: Snowflake, dbt, Airbyte, and cloud services across Azure/AWS.
Evangelize practical AI usage inside the company: share patterns, templates, examples, and “how-to” guidance that helps others adopt quickly and safely.
Iterate fast with feedback: ship v1 quickly, measure impact (time saved / risk reduced), then harden and scale what works.