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For Companies At a Glance
- Tasks: Join us to design and build backend features for an AI-driven SaaS platform.
- Company: We're a boutique consultancy creating innovative AI solutions on AWS.
- Benefits: Enjoy remote work flexibility with competitive pay and exciting projects.
- Why this job: Be part of a cutting-edge team shaping the future of AI technology.
- Qualifications: Proficiency in Python or Node.js, AWS experience, and LLM API knowledge required.
- Other info: Opportunity for multiple sprint projects after the initial build.
The predicted salary is between 36000 - 60000 ÂŁ per year.
Project: 4- to 6-week build sprint
Location: Remote (2–3 h overlap with UK time)
Fee: Fixed-price USD $4,000-$6,000
About Us
We’re a boutique consultancy spinning up an AI-enabled SaaS product. Our AWS backbone—multi-AZ VPC, ECS Fargate, ALB, Terraform IaC, logging/monitoring, IAM hardening, etc. is already in place. Your brief is to layer business logic, API endpoints and a small admin UI on top of this platform, integrating an LLM-driven scoring pipeline plus a few external APIs.
What You’ll Tackle
- Backend feature work
- Design and ship REST or GraphQL endpoints.
- Build multi-tenant data models and role-based workflows.
- Trigger emails and webhooks for status changes.
- Wire existing prompts to an LLM API (OpenAI-compatible today, private model later).
- Store embeddings in a vector store and perform RAG look-ups.
- Surface “explanation” JSON for every model decision.
- Deploy new services into the existing Terraform / ECS stack—extend, don’t rewrite.
- Add CloudWatch alarms, log routes and parameter-store secrets where needed.
- Write unit and integration tests and set up a lightweight CI pipeline.
- Provide concise hand-over docs and 30 days of post-launch bug-fix cover.
Must-Have Skills
- Pro in Python (FastAPI) or Node.js (TypeScript / Nest)—ready to justify your pick.
- Experience extending an AWS stack (Terraform, ECS Fargate, ALB, Secrets Manager, KMS).
- Hands-on with LLM APIs and at least one vector database (Pinecone, Weaviate, OpenSearch, etc.).
- Multi-tenant data design with GDPR awareness.
- CI/CD and automated-testing mindset.
Nice-to-Haves
- Solution-architect background—ability to map future services and scaling paths.
- Prior LLM fine-tuning or custom-model training.
- Chrome-extension data capture.
- Transactional-email tooling (SES, Postmark, Mailgun).
Following this sprint project there will be an additional 2-3 sprint projects of similar size to follow.
Back End Developer employer: Future_find
Contact Detail:
Future_find Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Back End Developer
✨Tip Number 1
Familiarise yourself with the specific technologies mentioned in the job description, such as Python (FastAPI) or Node.js (TypeScript/Nest). Being able to discuss your experience and preferences with these frameworks will show that you're a good fit for the role.
✨Tip Number 2
Highlight any previous experience you have with AWS services, especially Terraform, ECS Fargate, and ALB. Be prepared to share examples of how you've extended an AWS stack in past projects, as this is crucial for the role.
✨Tip Number 3
If you have worked with LLM APIs or vector databases, make sure to mention this during discussions. Sharing specific projects where you've implemented these technologies can set you apart from other candidates.
✨Tip Number 4
Demonstrate your understanding of multi-tenant data design and GDPR compliance. Being able to articulate how you've approached these topics in previous roles will show that you are not only technically skilled but also aware of important regulatory considerations.
We think you need these skills to ace Back End Developer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience with Python or Node.js, AWS stack, and LLM APIs. Use specific examples from past projects that demonstrate your skills in backend development and cloud services.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Mention your understanding of their AI-enabled SaaS product and how your skills align with their needs, particularly in building REST or GraphQL endpoints.
Showcase Relevant Projects: Include links to any relevant projects or GitHub repositories that showcase your work with multi-tenant data models, CI/CD pipelines, or integration with LLM APIs. This will provide tangible evidence of your capabilities.
Highlight Problem-Solving Skills: In your application, emphasise your problem-solving abilities, especially in relation to deploying services in an existing AWS stack and writing unit tests. Provide examples of challenges you've faced and how you overcame them.
How to prepare for a job interview at Future_find
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python (FastAPI) or Node.js (TypeScript/Nest). Highlight specific projects where you've implemented REST or GraphQL endpoints, and be ready to justify your choice of technology.
✨Demonstrate AWS Proficiency
Familiarise yourself with the AWS stack mentioned in the job description. Be ready to discuss how you've extended services using Terraform, ECS Fargate, and other AWS tools, as well as any challenges you faced and how you overcame them.
✨Understand LLM Integration
Since the role involves working with LLM APIs, brush up on your knowledge of these technologies. Be prepared to explain how you've previously integrated LLMs into applications and your approach to handling data models and workflows.
✨Prepare for Quality Assurance Discussions
The company values a CI/CD mindset, so be ready to talk about your experience with automated testing and setting up CI pipelines. Discuss any unit or integration tests you've written and how they contributed to project success.
