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# Full Stack- FDE EST

- **Company:** NewPage Digital Healthcare Solutions
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** JavaScript (Programming Language), Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Amazon Web Services, Application Integration Architecture, Microsoft Azure, Cloud Computing, Code Coverage, Software Quality, Encodings, Databases, Continuous Integration, Data Integration, Extract Transform Load (ETL), Relational Databases, Cursor (Graphical User Interface Elements), DevOps, Github, Python (Programming Language), PostgreSQL, Operational Databases, Scrum Methodology, Rapid Prototyping Process, Redis, Reliability Engineering, Next.js, Simple Data Format, Software Engineering, Systems Integration, TypeScript, Web Application Frameworks, GitHub Copilot, ReactJS, Large Language Models, Software Application Programming, Technical Debt, Generative AI, Backend, Cloudformation, Build Management, Containerization, Infrastructure Automation Frameworks, Information Technology, Front End Software Development, Terraform, Web Api
- **Published:** June 12, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=8a24df4c2a49d56a

## About the Role

Do you have experience in UI?, Do you have a Bachelor's degree?, * Bachelor's degree in computer science, Software Engineering, or related field with 3+ years of relevant professional experience.

 * Strong production experience with Python and JavaScript/TypeScript ,NextJS across backend and frontend.
 * Experience building applications involving MCPs, APIs, UI, and backend systems
 * Hands-on experience with modern frontend frameworks (such as Next.js or React) and backend API development.
 * Production experience with cloud platforms (AWS preferred and Cloudfare; Azure or GCP also valued), including infrastructure-as-code tools (such as CloudFormation or Terraform).
 * Working knowledge of multiple database paradigms, including relational
 * databases (such as PostgreSQL), document databases, and key-value stores (such as Redis).
 * Experience with CI/CD pipelines (such as GitHub Actions) and a strong
 * understanding of path-to-production practices.
 * Demonstrable fluency with AI coding tools (such as Claude Code, Cursor, GitHub Copilot, or similar) and experience building agentic engineering workflows.
 * Hands-on experience building production generative AI applications (LLM
 * integrations, vector databases, RAG systems) is essential.
 * Experience navigating ambiguous problem spaces, working directly with business stakeholders and end users, and shipping working solutions rapidly is strongly valued.
 * Experience in an embedded, forward-deployed, or consulting-style engineering model is a strong plus.

Nice to Have Skills

 * Experience working in Agile environments (Scrum/Kanban)
 * Strong collaboration with cross-functional teams
 * Ability to take ownership from ideation to delivery

## Description

* Delivery: Deliver working solutions rapidly - days not weeks. Use prototypes to build stakeholder trust, know when to stop prototyping and start productionising, and balance speed with appropriate quality. Deliver complete features end-to-end independently across frontend, backend, database, and infrastructure.
 * Generative AI: Design production RAG systems with appropriate chunking, embedding, and retrieval strategies. Optimise for relevance and latency, handle edge cases, and evaluate end-to-end system quality. Design evaluation frameworks with custom evaluators tailored to your use case. Build golden datasets and run experiments to compare prompt and model changes systematically.
 * People: Facilitate collaboration across the team, resolve minor conflicts before they escalate, and enable others to succeed. Present complex topics clearly to any audience. Translate between technical and business language fluidly and write compelling proposals and specifications.
 * AI-Augmented Development: Integrate AI tools strategically into your development workflow and that of the team. Review AI-generated code with the same rigour as human code and never ship code you don't fully understand. Develop team practices that balance AI speed with verification rigour.
 * Scale: Design components and services independently for moderate complexity. Make appropriate trade-off decisions, document design rationale, and consider AI integration points in your designs. Produce consistently high-quality, well-tested code and review AI- generated code critically, identifying edge cases and ensuring adequate test coverage.
 * Documentation: Create comprehensive documentation for complex systems. Write precise specifications that enable accurate AI-generated code, establish documentation practices for your projects, and ensure docs are discoverable. Identify patterns across implementations and propose candidates for generalization.
 * Reliability: Design observability strategies for your services. Lead incident response, implement resilience testing, and conduct blameless post-mortems. Balance reliability investment with feature velocity.
 * Process: Optimise team processes systematically, measure and improve cycle time, remove bottlenecks proactively, and deliver rapidly while maintaining quality. Protect focused time for complex work while balancing multiple priorities effectively

Role Behaviours

 * Own the Outcome: Take end-to-end ownership of features and business outcomes. Accept technical debt intentionally when it accelerates value delivery. Build trust through rapid delivery of working solutions. Own stakeholder relationships and balance quality with delivery speed. AI may generate the code, but responsibility for outcomes remains with you.
 * Be Polymath Oriented: Bridge gaps between engineering, design, business, and science. Rapidly immerse in new domains. Speak the language of Commercial operations and make better decisions by understanding the broader business context. See connections across disciplines that others miss.
 * Communicate with Precision: Separate requirements, designs, and tasks with precision. Enable AI to generate accurate code through clear specifications. Translate between technical and business language fluently. Facilitate productive discussions and reduce ambiguity in everything you communicate.
 * Don't Lose Your Curiosity: Proactively investigate root causes. Experiment with new technologies and AI capabilities. Protect time for exploration and treat failure as learning. Discover requirements through immersion in problem spaces rather than waiting for specifications.
 * Think in Systems: Map complex system interactions across technical and business domains. Anticipate cascading effects of changes. Design systems that degrade gracefully. Understand how technology changes impact business operations and factor this into your decisions.

Working-level Skills

 * Full-Stack Development: You deliver complete features end-to-end independently - frontend, backend, database, and infrastructure. You make pragmatic technology choices and deploy what you build.
 * Architecture & Design: You design components and services independently for moderate complexity. You make appropriate trade-off decisions, document design rationale, and consider AI integration points in your designs.
 * Code Quality & Review: You produce consistently high-quality, well-tested code. You review AI-generated code critically and never ship code you don't fully understand. You identify edge cases and ensure adequate test coverage.
 * Problem Discovery: You navigate ambiguous problem spaces independently. You discover requirements through observation and user shadowing, reframe problems to find higher-value solutions, and distinguish symptoms from root causes.
 * Rapid Prototyping & Validation: You deliver working solutions rapidly (days not weeks). You use prototypes to build stakeholder trust, know when to stop prototyping and start productionising, and balance speed with appropriate quality.
 * Retrieval Augmentation: You design production RAG systems with appropriate chunking, embedding, and retrieval strategies. You optimise for relevance and latency, handle edge cases, and evaluate end-to-end system quality.
 * AI-Augmented Development: You integrate AI tools strategically into your development workflow. You review AI-generated code with the same rigour as human code and never ship code you don't fully understand.
 * Multi-Audience Communication: You present complex topics clearly to any audience, facilitate productive discussions, translate between technical and business language fluidly, and write compelling proposals and specifications.
 * Business Immersion: You apply deep domain knowledge to technical solutions,
 * bridge business and technology conversations fluently, speak the domain language naturally, and shadow operations to build understanding.

Foundational-level Skills

 * Stakeholder Management: You proactively update stakeholders on progress, handle basic expectation setting, and escalate concerns appropriately. You build rapport with regular collaborators and manage expectations around delivery timelines.
 * DevOps & CI/CD: You configure basic CI/CD pipelines, understand containerisation, and can troubleshoot common build and deployment failures.
 * Cloud Platforms: You deploy applications to cloud platforms and use common services (compute, storage, databases, queues). You understand cloud pricing and basic security configuration.
 * AI Evaluation & Observability: You instrument applications with tracing to capture execution flow. You create evaluation datasets from production data, run basic LLM-as-judge evaluations, and apply pre-built evaluators for common metrics like faithfulness and relevance.
 * Technical Debt Management: You document technical debt with context and business impact, contribute to prioritisation discussions, and address debt in code you touch.
 * Team Collaboration: You collaborate effectively on shared work, support teammates proactively, share knowledge freely, and give and receive feedback constructively. You help onboard new team members.
 * Data Integration: You create simple data transformations and handle common data formats. You identify and report data quality issues and understand basic ETL concepts.
 * Pattern Generalization: You identify patterns across multiple implementations
 * and propose candidates for generalization. You understand the trade-offs between custom and reusable solutions.

Awareness-level Skills

 * Site Reliability Engineering: You understand SLIs, SLOs, and error budgets conceptually. You can use monitoring dashboards and escalate issues appropriately.
 * Service Management: You understand service lifecycle concepts (design, deploy, operate, retire) and follow service management processes established by others.
 * AI Literacy: You understand basic AI concepts (training, inference, prompts) and
 * can recognise AI-powered features in products. You know AI has limitations and when traditional approaches may be better.
 * Data Modelling: You understand the difference between relational and non-
 * relational data stores. You can create basic schemas from specifications with guidance.


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