Offer summary

Qualifications:

5+ years of backend experience in Python, specifically with FastAPI framework., 2+ years of experience in AI/ML-driven systems, particularly with LLM integration or RAG systems., Strong understanding of vector similarity search and experience with vector store integrations., Bachelor's or Master’s degree in Computer Science, AI, or a related field..

Key responsibilities:

Design and build robust, secure, and scalable APIs and microservices for LLM-powered applications using FastAPI.

Develop modular RAG pipelines, integrating vector databases with custom retrieval logic.

Optimize inference speed and memory usage for multi-modal and NLP-based systems in production.

Collaborate with team members to ensure alignment on system design and project requirements.

Job description

🌟 Senior Backend Engineer – Generative AI

📍 Location: Remote
🕒 Type: Project-Based | Senior-Level
🧠 Domain: Generative AI, LLMs, RAG Systems, Cloud-Scale APIs, FastAPI

CLICK HERE TO DO THE INTERVIEW: https://app.10folders.com/en/interview-playground/69ef89f04df2411482ea880648b0a4e5

Project Engagement Overview

Scope: Develop the entire backend and AI-agent layer for a greenfield chatbot platform, including chatbot services, system architecture, database, and integrations.
Compensation Model: Fixed-price contract between USD 4,000 – 8,000 (final amount to be determined post-discovery).
IP & Ownership: All source code, documentation, prompts, and architecture artifacts will be the exclusive property of our company. The engineer may not reuse, resell, or share any part of the work without prior written permission.
Warranty & Bug-Fix Period: You’ll remain available for up to 3 months after project handoff to fix bugs or fill any functional gaps, free of charge.

💰 Payment Terms

Milestone-based payments aligned with the schedule defined during Discovery.
A typical breakdown (flexible):
- 20% — After architecture sign-off and milestone agreement.
- 35% — Midway checkpoint (50% functionality delivered).
- 35% — Code completion and staging deployment.
- 10% — Upon final acceptance / end of warranty period.

🔒 Quality, Review & Standards

Code should be clean, maintainable, and scalable, adhering to best practices (e.g., SOLID principles, DRY, comprehensive README).
We’ll conduct regular code reviews; feedback must be addressed promptly.

🤝 Collaboration & Communication

Work closely with our Business Owner to understand the use case, confirm requirements, and align tech decisions with business goals.
Collaborate continuously with our System Architect on system design and database structure; they may review your PRs and progress.
Maintain close coordination with our Front-End engineer during development, communication should be open and proactive.
A DevOps engineer will assist around deployment time; expect cooperative handoff.
Be available for 2–3 hours of timezone overlap with GMT+3 (Palestine) for real-time discussions (exact hours TBD).

📑 Documentation & Handover

Submit complete setup instructions, environment configuration, and API documentation (Swagger/OpenAPI or equivalent).
Share entity-relationship diagrams (ERDs) and system architecture visuals using tools like dbdiagram.io, draw.io, Miro, or Lucidchart.
When needed, record short Loom or screen-share walkthroughs to explain complex modules.

🙌 Key Responsibilities

Design and build robust, secure, and scalable APIs and microservices for LLM-powered applications using FastAPI.
Develop modular RAG pipelines, integrating vector databases (Milvus, or Qdrant) with custom retrieval logic.
Work with models like LLaMA, Mistral, GPT, and embedding models (Nomic, OpenAI, HuggingFace).
Optimize inference speed and memory usage for multi-modal and NLP-based systems running in production.
Celery for task queues, and monitoring (Flower, Prometheus).
Handle authentication & authorization with JWT, OAuth, and secure role-based access control (RBAC).
Utilize Docker for the project.
Bouns: Architect cloud-native AI backends on AWS/GCP/Azure, leveraging services like S3, EC2, Lambda, Secrets Manager, and Docker.
Bouns: Ensure observability and reliability with CI/CD pipelines (GitHub Actions).

🎯 Requirements

5+ years of backend experience in Python (FastAPI Framework).
2+ years working in AI/ML-driven systems or products, ideally with experience in LLM integration or RAG systems.
Experience with Postgres and one of FastAPI ORM such as SQLAlchemy-Almbic.
Experience with LangChain, LangGraph libraries.
Strong understanding of vector similarity search and vector store integrations.
Solid experience with containerization (Docker) and orchestrating distributed systems.
Familiarity with data serialization, async programming, rate limiting, and secure API design.
Excellent debugging, documentation, and system design skills.
Bachelor's or Master’s in Computer Science, AI, or related field.