This is a remote position.

Job Title: Back-End Software Engineer Location: Remote Team: AI Infrastructure & Engineering Employment Type: Full-Time *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?

If this opportunity excites you and your skills align with the role, we'd love to learn more about you.

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.

Start the interview here:  https://interviews.apriora.ai/remoteworkmate-back-end-software-engineer-4rxg *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: First 30 Days

Set up staging and dev environments

Review codebase and system architecture

Deploy test API integrating a basic OpenAI or HF model

By Day 60

Launch a production-ready AI feature (e.g., vector store or RAG endpoint)

Improve model response latency by 30–50%

Implement >80% test coverage

By Day 90

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 Uptime ≥ 99.5% on core services

Test coverage > 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

Requirements RESPONSIBILITIES & DUTIES Architect and maintain APIs that serve AI/LLM-powered services with sub-second latency Deploy, fine-tune, and manage OpenAI, Hugging Face, or open-source models for live use cases Build scalable pipelines for vector embeddings, semantic search, and retrieval-augmented generation (RAG) Implement observability (logging, metrics, tracing) to support real-time debugging and performance insights Optimize infrastructure performance via caching layers, async frameworks, or resource scaling Design and maintain DevOps workflows for CI/CD, infrastructure-as-code, and production deployments Collaborate cross-functionally with ML engineers, product owners, and designers to ship and scale features Continuously document architecture, workflows, and improvement initiatives in async-first platforms REQUIREMENTS 3+ years in back-end/API development with a focus on performance and reliability Advanced Python experience with FastAPI or Flask Strong in PostgreSQL and Docker-based development environments Proven use of OpenAI APIs, Hugging Face Transformers, or LangChain in real-world apps Familiar with vector databases (e.g., Pinecone, Qdrant, Weaviate) Hands-on experience in CI/CD pipelines using GitHub Actions, Terraform, or similar Comfortable building in async environments using asyncio, aiohttp, or equivalents Clear, documented, and self-driven: able to work independently in remote, async environments