Full-stack Engineer (Generative AI Prototyping & MVP) at career

📍 Job Overview

Job Title: Full-stack Engineer (Generative AI Prototyping & MVP)

Company: career

Location: North & South America

Job Type: Contract

Category: Engineering - Full-Stack, Generative AI, Prototyping

Date Posted: 2025-06-13T00:00:00

Experience Level: 3+ years

Remote Status: Remote

🎨 Role Summary

Lead the rapid design and development of functional prototypes and early Minimum Viable Products (MVPs) for a new Generative AI product, IrisGo.ai.
Work across the full stack to build and ship innovative AI-powered user experiences with a focus on speed and clarity in the design and development process.
Apply core Generative AI building blocks, including Prompt Engineering, Retrieval-Augmented Generation (RAG), and Vector Databases, to solve real-world product use cases.
Collaborate closely with founders, product thinkers, and engineers to transform abstract design concepts into tangible, testable, and scalable applications.

📝 Enhancement Note: While the title is 'Full-stack Engineer', the strong emphasis on prototyping, MVP development, and Generative AI application suggests a role deeply intertwined with the design and iterative development process. The engineer will effectively be a key player in bringing design concepts to life using cutting-edge AI technologies. The category has been enhanced to reflect this focus.

🖼️ Primary Responsibilities

Rapidly design, develop, and deploy full-stack applications serving as functional prototypes and early MVPs for IrisGo.ai, ensuring a fast iteration cycle from concept to user testing.
Implement and integrate Generative AI capabilities into user experiences, leveraging techniques such as Prompt Engineering, RAG, Vector Databases (e.g., Pinecone, Weaviate), and Agent frameworks (e.g., LangGraph, CrewAI).
Utilize AI-assisted development tools (e.g., Cursor, Copilot, Windsurf) to significantly accelerate the coding, design, and prototyping processes, improving development efficiency.
Deploy applications to cloud environments (AWS, GCP, Supabase, etc.), manage development environments, and ensure seamless integration with external APIs, LLM platforms, and third-party services.
Actively incorporate user feedback and data into the development and design process to quickly iterate on usability, functionality, and the overall user experience of the prototypes and MVPs.
Stay abreast of emerging Generative AI technologies, product patterns, and design trends in the AI space to inform development decisions and identify new opportunities for innovation.
Balance the need for rapid, "scrappy" development for experimentation with the strategic consideration of scalability and maintainability for future growth.

📝 Enhancement Note: The core responsibilities highlight a highly iterative and design-adjacent engineering role. The use of "rapidly design and develop" implies a close relationship with the design process, even if the primary title is engineering. The focus on incorporating user feedback further solidifies this connection to user-centered design principles.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Engineering, or a related field is typically preferred, though equivalent practical experience and a strong portfolio demonstrating Generative AI application development are highly valued.

Experience: Minimum of 3+ years of hands-on full-stack development experience, with a significant focus on building AI-powered products or prototypes. Experience in a fast-paced, iterative development environment, ideally within startups or innovation labs, is a strong asset. A portfolio showcasing built Generative AI applications is crucial.

Required Skills:

Strong foundational knowledge and practical experience with Generative AI building blocks, including Prompt Engineering, Retrieval-Augmented Generation (RAG), Vector Databases (e.g., Pinecone, Weaviate), Agent frameworks (e.g., LangGraph, CrewAI), and evaluation/guardrail tools.
Proficiency in front-end web development using JavaScript/TypeScript, HTML, CSS, and modern frameworks such as React or Vue, with an ability to translate design mockups into functional user interfaces.
Expertise in back-end development using Python and frameworks like FastAPI or Flask, including designing and implementing RESTful APIs and handling data processing.
Experience with database systems, including both SQL (e.g., PostgreSQL) and NoSQL databases (e.g., MongoDB), and the ability to design and optimize database schemas for AI applications.
Solid understanding of Git and modern development workflows, including continuous integration and continuous deployment (CI/CD) practices.
Experience with cloud infrastructure and deployment tools (e.g., AWS, GCP, Supabase), including deploying and managing applications in a cloud environment.
Hands-on experience utilizing AI-assisted development tools like Cursor, Copilot, or Windsurf to enhance coding efficiency and prototyping speed.

Preferred Skills:

Proven experience in launching Minimum Viable Products (MVPs) in startup or innovation-focused settings, demonstrating the ability to bring products to market quickly.
Familiarity with rapid prototyping tools or no-code/low-code development environments to quickly test and validate design ideas.
A strong interest in early user testing and a willingness to actively participate in shaping product direction based on user feedback.
Contributions to open-source projects related to Generative AI or development tooling.
Experience with serverless architectures or edge deployment strategies for building scalable and efficient applications.

📝 Enhancement Note: While this is an engineering role, the "Preferred Skills" section includes "Familiarity with rapid prototyping tools or no-code/low-code environments" and "Interest in early user testing and shaping product direction." These points strongly align with design principles and iterative product development cycles common in design roles. The emphasis on a portfolio showcasing built AI applications is also crucial for demonstrating practical design and development capabilities.

🎨 Portfolio & Creative Requirements

Portfolio Essentials:

Include case studies or examples of full-stack applications you have built that leverage Generative AI, demonstrating your ability to integrate AI components into functional products.
Showcase your design thinking process within your portfolio, highlighting how you approach problem-solving, user needs, and iterative development in the context of AI applications.
Present the visual design and user experience aspects of your projects, even if the primary focus is on the engineering implementation, demonstrating an understanding of user interface and interaction design.
Provide clear technical documentation for your projects, explaining the AI models used, the architecture, key technologies, and any custom code or integrations.

Process Documentation:

Document the research and discovery phase for your AI projects, including how you identified relevant use cases and gathered data for training or fine-tuning models.
Illustrate your ideation and iteration methods, showing how you experimented with different AI approaches, model configurations, and user interface designs.
Detail your validation and testing approaches, including how you evaluated the performance of the AI models, conducted user testing, and incorporated feedback into subsequent iterations.

📝 Enhancement Note: Although this is primarily an engineering role, the nature of prototyping and MVP development, especially in the context of user-facing AI, necessitates a strong understanding and demonstration of the design process. The portfolio requirements are tailored to emphasize the intersection of engineering and design in building functional, user-centered AI applications.

💵 Compensation & Benefits

Salary Range: Given the contract nature of the role and the focus on rapid prototyping in Generative AI, the estimated hourly rate for a Full-Stack Engineer with 3+ years of experience in North & South America with AI expertise could range from $70 to $120+ per hour, depending on the specific location, depth of AI experience, and negotiation. This estimate is based on industry benchmarks for contract roles in the Generative AI and software development space in major tech hubs across the Americas, adjusted for the specialized skill set required.

Benefits: While specific benefits for contract roles can vary, typical offerings might include:

Competitive hourly rate commensurate with experience and expertise in Generative AI development.
Flexibility of a remote work arrangement, allowing for a better work-life balance and the ability to work from various locations within North & South America.
Opportunity to work on cutting-edge Generative AI projects and gain valuable experience in a rapidly evolving field.
Potential for future engagement or conversion to a full-time role based on project success and performance.

Working Hours: This is likely a full-time contract position, implying a standard work week of approximately 40 hours. However, the rapid prototyping nature may require flexibility and responsiveness to meet project milestones and incorporate feedback quickly.

📝 Enhancement Note: As no salary information was provided, a salary range has been estimated based on the role's title, experience level, the specialized nature of Generative AI skills, contract employment type, and the broad geographic location (North & South America). The methodology involves researching average contract rates for Full-Stack Engineers with AI/ML experience in major tech markets within the specified regions and adjusting for the specific focus on prototyping and MVP development.

🎯Team & Company Context

🏢 Company & Design Culture

Industry: The organization, "career" which is associated with DeepLearning.AI, operates within the Software Development industry with a strong focus on Artificial Intelligence, Deep Learning, and Machine Learning education and application. This context suggests a culture deeply rooted in technical expertise, innovation, and the practical application of AI technologies.

Company Size: DeepLearning.AI has between 11-50 employees according to LinkedIn. Working in a smaller organization like this for a contract role implies a fast-paced, agile environment where individuals have a high degree of autonomy and impact. Collaboration is likely close-knit across functions, including design and engineering.

Founded: DeepLearning.AI was founded in 2017. This history suggests a company that has been at the forefront of AI education and is now expanding into applying that knowledge to build new AI products.

Team Structure:

Given the company size and contract nature, the team working on IrisGo.ai is likely a small, dedicated group focused on rapid product development.
Reporting structure is likely flat, with direct communication lines to founders and product leadership to facilitate quick decision-making.
Cross-functional collaboration between engineering, product, and potentially design (if a dedicated design resource is involved) will be essential for successful prototyping and MVP development.

Methodology:

The emphasis on "rapid development of prototypes and the first MVP" suggests an agile or lean development methodology, prioritizing speed, iteration, and responsiveness to feedback.
Design and development processes will likely involve quick cycles of ideation, building, testing, and learning, with a strong focus on user validation.
The application of Generative AI building blocks implies a technical methodology centered around prompt engineering, data handling for RAG, and integration of AI models.

Company Website: http://DeepLearning.AI

📝 Enhancement Note: The company context is inferred from the provided LinkedIn data for DeepLearning.AI, which is associated with "career". The small company size, founding date, and industry focus on AI strongly suggest the described team structure, methodology, and culture. The emphasis on rapid prototyping aligns with the typical approach of smaller, innovation-driven companies.

📈 Career & Growth Analysis

Design Career Level: Although this is an engineering role, the focus on prototyping and MVP development positions this role at the intersection of engineering and product design. For an engineer interested in product development and bringing design concepts to life with AI, this role offers significant hands-on experience in the early stages of product creation.

Reporting Structure: As a contract role in a small team, you would likely report directly to a technical lead, product manager, or potentially a founder. This direct reporting structure facilitates rapid iteration and clear communication on design and technical requirements.

Design Impact: Your technical contributions will have a direct and significant impact on the user experience and functionality of the IrisGo.ai product. By rapidly building and iterating on prototypes, you will play a key role in shaping the initial design and capabilities of the product based on user feedback and technical feasibility.

Growth Opportunities:

Gain extensive experience in applying cutting-edge Generative AI technologies to real-world product challenges, significantly enhancing your technical skill set in a high-demand area.
Develop expertise in the end-to-end process of bringing an AI-powered product from concept to MVP, including design translation, development, deployment, and iteration based on user feedback.
Opportunity to work closely with experienced founders and engineers in the AI space, providing valuable mentorship and networking opportunities.

📝 Enhancement Note: The career analysis focuses on how this engineering role intersects with design and product development, offering growth opportunities relevant to engineers interested in product creation and AI application. The impact on the product's design and functionality is a key aspect highlighted due to the prototyping focus.

🌐 Work Environment

Studio Type: This is a remote position, offering the flexibility to work from anywhere within North & South America. While there isn't a physical "studio," the work environment is likely highly collaborative online, utilizing various digital tools for communication and project management.

Office Location(s): The role is remote, so there are no specific physical office locations required for the contractor. The company's headquarters are in Palo Alto, California, but this role is not tied to that location.

Design Workspace Context:

The remote nature requires a self-disciplined and organized approach to work, ensuring a dedicated workspace conducive to focused design and development tasks.
Effective communication and collaboration tools will be essential for interacting with the team, sharing design progress, and receiving feedback.
Access to reliable internet and appropriate hardware/software for development and prototyping will be necessary.

Work Schedule: The nature of rapid prototyping and MVP development may involve flexible working hours to accommodate collaboration with team members across different time zones within North & South America. While a standard 40-hour week is expected, adaptability to project needs is likely required.

📝 Enhancement Note: The work environment details are based on the explicit "Remote" status and the broad geographic requirement. The "Design Workspace Context" is adapted to a remote setting, focusing on the tools and self-management skills necessary for effective remote collaboration and creative work.

📄 Application & Portfolio Review Process

Design Interview Process: While this is an engineering role, expect the interview process to heavily involve discussions around your experience in building user-facing applications and applying AI. For design-focused candidates, or engineers with a strong interest in product design, the process might include:

An initial screening call focusing on your experience with full-stack development and Generative AI, including a discussion of your portfolio projects related to AI applications.
A technical interview or coding challenge that may involve building a small prototype or demonstrating your ability to integrate AI components into an application.
A discussion of your approach to rapid prototyping and MVP development, including how you incorporate user feedback and iterate on design and functionality.
Conversations with team members and leadership to assess your collaboration skills, problem-solving abilities, and cultural fit within a fast-paced, innovation-focused environment.

Portfolio Review Tips:

Clearly highlight projects in your portfolio that involve building functional applications using Generative AI, showcasing the user interface, key features, and the AI components used.
For each relevant project, provide a brief case study outlining the problem you addressed, your approach (including design thinking and technical implementation), the tools and technologies used, and the outcome or impact.
Be prepared to walk through your code and explain your technical decisions, especially regarding the integration and application of Generative AI building blocks.
Demonstrate your understanding of the user experience in your projects, even if you weren't the primary designer, by discussing how your technical choices supported the intended user flow and interface.

Challenge Preparation:

If a technical challenge is provided, it will likely involve building a small application or feature that utilizes Generative AI. Focus on demonstrating your ability to quickly build a functional prototype that addresses the core requirements.
Be prepared to explain your thought process, design decisions (related to the application's structure and user interaction), and technical implementation choices during the challenge debrief.
Practice integrating common Generative AI components (like using an LLM API, implementing RAG, or working with a vector database) to ensure you can rapidly build relevant features.

ATS Keywords: Full-Stack Engineer, Generative AI, Prototyping, MVP Development, Python, FastAPI, Flask, JavaScript, TypeScript, React, Vue, SQL, NoSQL, PostgreSQL, MongoDB, Git, Cloud Deployment, AWS, GCP, Supabase, Prompt Engineering, RAG, Retrieval-Augmented Generation, Vector Database, Pinecone, Weaviate, Agent Frameworks, LangGraph, CrewAI, AI-Assisted Development, Cursor, Copilot, Windsurf, API Integration, LLM Platforms, Third-Party Services, User Feedback, Iteration, Scalability, Rapid Prototyping, No-Code, Low-Code, User Testing, Product Direction, Open Source, Serverless, Edge Deployment, Software Development, Artificial Intelligence, Deep Learning, Machine Learning, Product Development, User Experience, UI, UX, Design Thinking, Case Study, Portfolio.

📝 Enhancement Note: The application and interview process details are inferred based on the role's technical requirements, the emphasis on prototyping and MVP development, and the typical hiring practices for engineering roles in fast-paced tech environments. The advice on portfolio review and challenge preparation is tailored to highlight the intersection of engineering skills with the ability to build user-facing, AI-powered applications.

🛠 Tools & Technology Stack

Primary Design Tools: While this is an engineering role, the rapid prototyping focus implies interaction with design outputs. Proficiency in understanding and implementing designs created in tools like Figma, Sketch, or Adobe XD might be beneficial, although direct design tool usage is not explicitly required.

Front-end Frameworks: React or Vue (High proficiency expected for building user interfaces).
Back-end Frameworks: Python with FastAPI or Flask (Expertise required for building APIs and business logic).
Programming Languages: JavaScript/TypeScript, Python (Core languages for the role).
Version Control: Git (Essential for collaborative development and code management).

Collaboration & Handoff:

Collaboration tools like Slack, Microsoft Teams, or similar platforms for real-time communication and team interaction.
Project management tools such as Asana, Jira, Trello, or Notion for task tracking, progress monitoring, and potentially documenting design requirements and technical specifications.
Understanding of design-to-development handoff processes, potentially involving tools like Zeplin or Figma's developer handoff features, even if direct use is minimal.

Research & Testing:

Tools for integrating and utilizing LLM platforms and external APIs for Generative AI functionalities.
Experience with Vector Databases (e.g., Pinecone, Weaviate) for implementing RAG and semantic search capabilities.
Potential use of testing frameworks for both front-end and back-end code to ensure application stability and functionality.
Familiarity with tools for deploying and managing applications in cloud environments (AWS, GCP, Supabase).

📝 Enhancement Note: The tools and technology stack are primarily based on the explicit requirements listed in the job description. The "Primary Design Tools" section is included to acknowledge the potential interaction with design outputs in a prototyping role, even though direct design tool proficiency isn't a core requirement for this engineering position.

👥 Team Culture & Values

Design Values: While not explicitly a design role, the company's association with DeepLearning.AI and focus on building an AI product suggests a culture that values:

Innovation: A strong emphasis on exploring and applying cutting-edge AI technologies to create novel product experiences.
User-Centricity: A focus on building products that address real user needs and provide tangible value, likely involving iterative development based on user feedback.
Speed and Agility: A culture that prioritizes rapid iteration, experimentation, and quick delivery of functional prototypes and MVPs.
Technical Excellence: A commitment to building robust, scalable, and efficient applications leveraging best practices in software development and AI integration.

Collaboration Style:

Likely a highly collaborative environment with close interaction between engineering, product, and potentially design teams.
Open communication and a willingness to provide and receive feedback are likely valued for rapid iteration and problem-solving.
Cross-functional collaboration will be essential for translating product ideas and design concepts into functional code.

📝 Enhancement Note: The team culture and values are inferred from the company's background in AI education and its focus on building new AI products. The emphasis on rapid prototyping and MVP development strongly suggests a culture of innovation, speed, and user-centricity.

⚡ Challenges & Growth Opportunities

Design Challenges: Although an engineering role, challenges at the intersection of engineering and design may include:

Translating abstract Generative AI concepts and product ideas into tangible, user-friendly interfaces and experiences within a limited timeframe.
Rapidly iterating on the application's design and functionality based on user feedback while ensuring technical feasibility and scalability for the MVP.
Working with potentially evolving or ambiguous requirements inherent in the early stages of product development, requiring adaptability and proactive problem-solving.
Balancing the need for "scrappy" development for speed with the need to lay a foundation that can support future design iterations and scaling.

Learning & Development Opportunities:

Gain in-depth practical experience with a wide range of Generative AI building blocks and their application in real-world products.
Develop expertise in the end-to-end process of building and deploying AI-powered MVPs in a fast-paced startup environment.
Enhance your skills in rapid prototyping, iterative development, and incorporating user feedback into the development cycle.
Opportunity to contribute to the foundation of a new AI product and potentially grow with the company as it scales.

📝 Enhancement Note: The challenges and growth opportunities are identified by considering the nature of a rapid prototyping and MVP development role in the Generative AI space. The challenges are framed from an engineering perspective but highlight the areas where engineering intersects with design and product definition in this context.

💡 Interview Preparation

Design Process Questions: While the focus will be on engineering, be prepared to discuss your approach to building user-facing applications and how you consider the user experience. Questions might include:

Describe your process for taking a product idea or design concept and translating it into a functional application, especially in a rapid prototyping environment.
How do you incorporate user feedback into your development process? Provide examples of how feedback has influenced your technical or design decisions in past projects.
Discuss a project where you had to balance technical constraints with design requirements. How did you navigate those challenges?

Company Culture Questions: Research the company's association with DeepLearning.AI and its focus on AI. Questions might explore:

What interests you about working on a Generative AI product like IrisGo.ai?
How do you approach working in a fast-paced, potentially ambiguous environment where requirements may evolve quickly?
Describe a time you collaborated closely with product managers or designers to bring a feature to life.

Portfolio Presentation Strategy: Focus your portfolio presentation on projects that demonstrate your ability to build functional, user-facing applications with integrated AI components. For each project:

Clearly explain the problem the application solves and the role of Generative AI in the solution.
Walk through the user flow and highlight key features, demonstrating your understanding of the user experience.
Discuss the technical architecture, the specific AI models or techniques used, and the technologies you employed.
Be prepared to discuss the challenges you faced during development and how you overcame them, especially those related to AI integration or rapid iteration.

📝 Enhancement Note: The interview preparation advice is tailored to an engineering role with a strong emphasis on prototyping and AI application. It includes technical questions but also incorporates elements related to product development, user feedback, and collaboration, reflecting the nature of the role. The portfolio strategy emphasizes showcasing functional AI applications.

📌 Application Steps

To apply for this design position:

Submit your application through this link.
Prepare a portfolio that specifically showcases your experience in building full-stack applications that incorporate Generative AI, focusing on functional prototypes and user-facing features. Include case studies that detail your technical approach and the role of AI in the solution.
Optimize your resume by including relevant keywords from the ATS Keywords list, highlighting your experience with Generative AI building blocks, full-stack technologies (Python, JavaScript/TypeScript, React/Vue), and rapid prototyping methodologies.
Practice discussing your past projects, particularly focusing on the technical challenges you solved, the AI components you integrated, and how you approached building user-friendly applications. Be ready to explain your design and technical decisions.
Research the company's background in AI education and its potential vision for IrisGo.ai to demonstrate your interest and understanding of the domain during interviews.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and design industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Full-stack Engineer (Generative AI Prototyping & MVP)