Back-end Software Engineer

This is a remote position.

• Superstaffed.ai is part of Remote Workmate PTY LTD

About the Role:

We’re looking for a Back-End Software Engineer to architect and build high-performance infrastructure behind our AI-powered applications. This role sits at the intersection of software engineering and machine learning infrastructure. You’ll lead the development of APIs, vector databases, and scalable microservices that serve real-time intelligent responses using models like OpenAI and Hugging Face.

You’ll thrive here if you’re an autonomous problem solver who optimizes systems for speed, reliability, and cost—someone who thinks in automation and ships measurable results fast.

Ready to Apply?

You can begin the application process right away by completing a short, self-paced video interview with “Alex,” our AI interviewer. This helps us fairly assess your experience, communication style, and fit for the role.

• Note: Applications without a video interview will not be processed.

Responsibilities:

• Design and maintain APIs for AI-powered features (FastAPI, Flask)
• Integrate and fine-tune LLMs (OpenAI, Hugging Face, LangChain)
• Build pipelines for vector embeddings, semantic search, and RAG
• Optimize back-end systems for latency, scalability, and cost
• Collaborate with ML engineers to deploy and monitor inference systems
• Implement observability (Sentry, Prometheus, Grafana) for debugging
• Manage CI/CD and infrastructure-as-code (Docker, GitHub Actions, Terraform)
• Own full product verticals from API to deployment

Requirements:

• 3+ years in back-end/API engineering (Python, FastAPI/Flask)
• Experience with PostgreSQL, Docker, and containerized development
• Proven use of OpenAI APIs, Hugging Face, LangChain, or Transformers
• Familiar with vector databases like Pinecone, Qdrant, or Weaviate
• Experience in CI/CD, observability, and monitoring systems
• Bonus: Knowledge of asyncio, aiohttp, k8s, or serverless environments
• Strong communication, async-first documentation, and remote collaboration skills

Performance Milestones:

• Set up staging and dev environments
• Review codebase and system architecture
• Deploy test API integrating a basic OpenAI or HF model

• Launch a production-ready AI feature (e.g., vector store or RAG endpoint)
• Improve model response latency by 30–50%
• Implement >80% test coverage

• Own back-end infrastructure for a product line
• Reduce compute costs through caching/async strategies
• Contribute to LLM scaling roadmap

Success Metrics (KPOs):

• API latency 85%
• 1–2 production deployments per week
• LLM inference ? 3s with retries/failure handling

Tech Stack:

• AI Platforms: OpenAI, Hugging Face, LangChain
• Frameworks: FastAPI, Flask, SQLAlchemy
• Databases: PostgreSQL, Redis, Pinecone, Qdrant
• DevOps: Docker, GitHub Actions, Terraform
• Monitoring: Prometheus, Grafana, Sentry
• Collaboration: Slack, Notion, ChatGPT