Full Stack Developer / Product Engineer ONSITE -Scottsdale, Az

Full-Stack Developer / Product Engineer - LOCAL Candidates only

Public-Posting Performance-Based Hiring Profile

Build the whole product. Own the outcome. Pivot fast without losing momentum.

LOCATION

Scottsdale, Arizona | Full-Time | On-Site

SCHEDULE

Flexible start within the 7:00 a.m.-6:00 p.m. work window

COMPENSATION

$100,000-$130,000 base, depending on demonstrated capability

ROLE MIX

Approximately 50% front-end / 50% back-end development

CORE STACK

Python back end | TypeScript front end | AWS | APIs | Practical AI integrations

This is not a ticket-taking developer role. Envita is looking for a true full-stack builder who can move comfortably between front-end and back-end development, take an incomplete request from a non-technical stakeholder, clarify what actually needs to be built, and carry the solution through production. The role is approximately 50/50 front end and back end, with Python as the primary back-end language. AI is an important part of the environment, especially practical AI and API integrations, but this is not a machine-learning role and we do not want someone who relies on AI without understanding the code, architecture, risks, or output.

Position Purpose

The Full-Stack Developer / Product Engineer will build secure, reliable, cost-conscious products across Envita business units. The role requires strong full-stack range, direct stakeholder communication, rapid learning, practical AI integration experience, and a high degree of self-direction. A defining requirement is the ability to pivot immediately when business priorities change while preserving the state, visibility, and restart point of every other active project.

Why This Opportunity Stands Out

• End-to-end ownership from requirements and architecture through build, integration, deployment, testing, and production follow-through.
• True full-stack scope with meaningful work on both the user-facing front end and the Python back end.
• Direct access to the people using what you build, including clinicians, pharmacy and laboratory operators, business leaders, and executives.
• Practical AI work, including AI-provider APIs and AI-enabled product features, without requiring machine-learning specialization.
• Variety, autonomy, and visible business impact across multiple Envita entities rather than one narrow product lane.
• A flexible daily start window designed to support an on-site Scottsdale work environment.

What Exceptional Success Looks Like After the First Year

Envita leaders can bring this engineer an important business problem and trust that it will be clarified, translated into a practical technical solution, built across the full stack, deployed securely, and supported with clear status and documentation. The engineer is equally credible in front-end and Python back-end work, uses AI and external APIs intelligently, understands and can explain the code they ship, and is known for being able to change direction quickly when the business requires it without allowing lower-priority projects to disappear or restart from zero.

SMART Key Performance Objectives

These outcomes define success in the role. Demonstrated capability and comparable accomplishments matter more than years of experience, prior title, or exact framework overlap.

1. Deliver the First Full-Stack Product Fast

Required outcome: Take an assigned project from the initial stakeholder conversation through requirements clarification, front-end and Python back-end development, API integration as needed, testing, and production deployment. Target a meaningful demo by approximately day 21 and a production-ready release by approximately day 45.

Evidence of success: The stakeholder confirms the solution solves the intended problem; the engineer can explain the architecture and code; appropriate testing, authentication, secrets handling, logging, and production controls are in place; and the solution operates reliably after launch.

2. Pivot on a Dime Without Losing Momentum

Required outcome: Operate effectively across multiple active projects while being able to immediately redirect attention when leadership or business needs change. When one project becomes critical, shift the majority of effort to it without allowing other work to become invisible, confused, or difficult to restart.

Evidence of success: Priority changes are acted on quickly; stakeholders are informed; paused work has a documented current state, next action, blocker, and restart point; the critical project accelerates; and previously active projects resume without unnecessary rework or loss of context.

3. Own Requirements From Ambiguity to Agreement

Required outcome: Independently elicit, clarify, document, and confirm requirements with non-technical stakeholders, including senior leaders, before significant build work begins.

Evidence of success: A concise requirements summary and success criteria are confirmed; assumptions and trade-offs are surfaced; and significant rework caused by misunderstood requirements is minimized.

4. Perform as a True 50/50 Full-Stack Engineer

Required outcome: Contribute at a high level across both front-end and back-end development. Front-end work will center on modern TypeScript-based interfaces; back-end work will center on Python services, data, APIs, business logic, and integrations.

Evidence of success: The engineer independently delivers meaningful production work on both sides of the stack, can trace data and behavior end to end, and is not dependent on another developer to complete one half of the solution.

5. Build Secure, Cost-Conscious Cloud and API Integrations

Required outcome: Own deployment and integration decisions with appropriate security, observability, and cost discipline. Build and consume APIs safely, including third-party and internal services, while protecting sensitive data.

Evidence of success: Services use appropriate access controls, managed secrets, transport security, logging/observability, validated API boundaries, and sensible cloud architecture; security or cost issues are surfaced before release.

6. Use AI as a Force Multiplier Without Becoming Dependent on It

Required outcome: Apply practical AI experience to product development and integrations, including AI-provider APIs and AI-assisted development tools. Machine-learning model development is not required. Remain accountable for understanding, validating, testing, securing, and maintaining anything AI helps produce.

Evidence of success: The engineer can describe real AI/API work they personally shipped, explain input/output design and integration choices, validate model output, consider latency/cost/sensitive-data concerns, and independently debug or modify the implementation.

7. Maintain Code Quality, Testability, and Durable Engineering Artifacts

Required outcome: Read and modify unfamiliar code, create meaningful unit/integration tests, and document systems sufficiently for another engineer to deploy, troubleshoot, and extend them.

Evidence of success: New and materially modified code includes appropriate automated tests; regressions are avoided; the engineer can explain runtime behavior of unfamiliar code; and production projects include practical run/deploy and operational documentation.

Core Responsibilities

• Build and support full-stack applications with an approximately 50/50 split between front-end and back-end development.
• Develop Python back-end services, APIs, data workflows, integrations, and business logic.
• Build TypeScript-based front-end experiences that are usable, reliable, and tightly integrated with underlying services.
• Gather requirements directly from non-technical stakeholders and convert business outcomes into clear technical success criteria.
• Carry several projects concurrently and rapidly reprioritize when business needs change while preserving continuity on lower-priority work.
• Integrate internal and third-party APIs, including AI-provider APIs, and handle validation, retries, errors, authentication, sensitive data, and production reliability.
• Own AWS deployment, security fundamentals, logging/observability, secrets management, access controls, and reasonable cost discipline.
• Use AI development tools productively without allowing AI-generated output to substitute for engineering judgment, code comprehension, testing, or accountability.
• Read, debug, and modify unfamiliar code; trace real system behavior; and create test coverage where needed.
• Communicate status, trade-offs, risks, blockers, changing priorities, and delivery expectations clearly to technical and non-technical stakeholders.

Ideal Experience and Technical Foundation

Strong candidates will show evidence that they can independently perform work of comparable scope, move between front-end and back-end work, learn quickly, and stay effective under changing priorities.

• Strong production Python back-end development experience, including APIs, services, business logic, data access, error handling, and testing.
• Hands-on TypeScript front-end development with shipped user interfaces, component/state management, asynchronous data, forms, and API integration.
• Relational data modeling using Postgres or a comparable database, including schema design, indexing, transactions, and safe migrations.
• REST/API design and integration experience, including authentication/authorization, validation, versioning, idempotency, structured errors, and reliability concerns.
• Hands-on AWS deployment ownership, including IAM/access control, secrets, networking fundamentals, logs/observability, and cost awareness.
• Application-security fundamentals, especially authentication vs. authorization, injection/XSS awareness, secret handling, transport security, least privilege, and sensitive data.
• Unit and integration testing as a normal part of development, plus confidence standing up test coverage in existing or lightly documented systems.
• Git fluency and the ability to navigate an unfamiliar repository and determine what the code actually does.
• Meaningful practical AI experience, preferably integrating AI/LLM capabilities through APIs or building AI-enabled product features. Machine-learning model development is not required.
• A recent, specific example of learning a new language, SDK, API, framework, or platform under deadline and successfully shipping with it.

What Separates Top Performers

• Priority Agility: Can change direction immediately when business priorities change, make the new priority successful, and preserve clean restart points for everything temporarily deprioritized.
• Full-Stack Range: Is genuinely comfortable delivering both user-facing front-end work and production Python back-end services.
• AI Judgment: Uses AI where it improves speed or quality, but never substitutes AI output for understanding, verification, security, or accountability.
• Requirements Discipline: Asks clarifying questions before coding, confirms understanding, and avoids building the wrong thing quickly.
• Self-Direction: Creates structure when structure is light, keeps multiple projects visible, and escalates conflicts before they become surprises.
• Ship-Quality Judgment: Moves quickly while knowing which shortcuts are acceptable and which are not, especially around security, sensitive data, authentication, data integrity, and cost.
• Constructive Communication: Explains choices and trade-offs clearly and responds to changed priorities without defensiveness or loss of follow-through.

The Environment

This role is best suited to an engineer who enjoys ownership, variety, speed, and incomplete problems. Requirements may arrive verbally. Priorities can change during the week or during the day. An engineer may be progressing several projects and then be asked to focus almost entirely on one urgent initiative. Success requires flexibility without chaos: make the pivot, communicate it, preserve the state of the other work, and keep commitments visible. There may not always be a mature product-management process, complete design handoff, dedicated DevOps support, or perfect documentation waiting before work begins.

Culture and Working Style

• Patient and business impact comes before technology for technology's sake.
• Integrity, humility, gratitude, learning agility, respectful collaboration, confidentiality, and accountability matter in how the work gets done.
• Strong performers are comfortable receiving direct feedback, revisiting assumptions, and changing course when the evidence or business priority changes.
• We value people who are curious, dependable, solution-oriented, technically accountable, and willing to own the consequences of their decisions.