Design and deploy scalable AI pipelines, vector search systems, and LLM-powered features on cloud platforms. Develop end-to-end workflows and ensure model efficiency at enterprise scale. Work with cloud-native deployment and modern cloud stacks.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
About Asite
We start with a simple idea: the built environment should be smarter, safer and more sustainable. Everything we do is about helping the people behind major construction and infrastructure projects work together more easily and make better decisions.
Asite offers a cloud-based platform that connects project teams, improves collaboration and manages data from the first design to the final handover. Industry leaders such as Laing O’Rourke, Transport for London, MTA New York and Aldar use Asite to keep their projects running smoothly and delivering strong results.
With offices around the world and a record of steady, profitable growth, we are shaping the future of construction technology while supporting the people who build the world around us.
The Role
You’ll work across our cloud ecosystem to build scalable AI pipelines, vector search systems, and LLM-powered features. You will design end-to-end workflows — ingestion, embeddings, retrieval, inference — and ensure our models run efficiently at enterprise scale.
What You’ll Work On
- Build and deploy AI/LLM pipelines on Vertex AI and Azure AI
- Develop vector search architecture (global index + user-level sub-collections)
- Create ingestion workflows for documents, files, and structured data
- Design embedding pipelines (batch + streaming)
- Implement metadata filtering, ACL logic, and hybrid search
- Deploy inference endpoints (GPU/CPU autoscaling on GCP/Azure)
- Integrate AI layers with existing backend APIs
- Monitor performance, quotas, and scaling for AI services
What You Bring
- 3+ years as an AI/ML/LLM engineer
- Hands-on experience with Vertex AI, Azure AI Studio, or Azure ML
- Strong Python (PyTorch, Transformers, FastAPI)
- Experience with vector databases / vector search systems
- Understanding of RAG, embeddings, hybrid search
- Cloud-native deployment (Cloud Run, Functions, Pub/Sub, Event Grid)
Nice to Have
- OpenAI / Azure OpenAI / Gemini experience
- GPU optimisation (T4, L4, A40)
- CI/CD for ML systems
- Kubernetes exposure
Why Join Us
- Fully remote role
- Opportunity to shape AI architecture from the ground up
- A role with big ownership and visible impact
- Modern cloud stack (GCP + Azure + LLMs)
- Strong team, supportive culture, and room to grow
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