AI Architect Engineer (Hands-on coder) WFH

Read everything carefully. The requirements and screening questions are critical and if not answered correctly and satisfactorily will result in auto-rejection and waste of your time.

• Work from Home.
• This is a full-time role. If you plan to do 2 or more jobs at the same time or want to do this part-time, that won't work for us. In that case please do not apply as it will get auto-rejected
• Note - this job requires working late night India time until 4AM to overlap with USA working times. Do not apply if this timing doesn't work
• Salary depends on experience and current verifiable (paychecks) compensation.
• Mid-level candidates with 3-5 years experience are suitable

AI Architect (Hands-On) — Multi-Agent AI Platform

About Qubrid AI

Qubrid AI is building next-generation AI infrastructure focused on inference, GPUs, multi-model orchestration, and scalable AI deployments. Our mission is simple: democratize access to AI infrastructure - from developers spending their first $5 to enterprise-scale AI deployments processing billions of inference requests. We are looking for a deeply technical AI Architect who can design and build production-grade AI systems end-to-end, not just create architecture diagrams.

This role is for builders:

• You should be equally comfortable:
• writing production Python code
• optimizing inference pipelines
• working with open-source models
• building multi-agent systems
• designing scalable backend architectures
• deploying AI systems into production
• If you are primarily theoretical or management-focused, this role is probably not the right fit.

What You'll Build

You will help architect and build a full-stack multi-agent AI SaaS platform including:

• Multi-agent orchestration systems
• AI inference pipelines
• Fine-tuning workflows
• RAG systems
• Tool-calling architectures
• Memory and context management systems
• Model routing and optimization layers
• Backend APIs and distributed systems
• GPU-aware inference infrastructure
• Enterprise-grade scalable deployments
• This is a highly hands-on engineering role where architecture and implementation go together.

Responsibilities

• AI Systems & Multi-Agent Architecture
• Design and build production-grade multi-agent AI systems
• Develop orchestration frameworks for autonomous workflows
• Implement agent communication, memory, planning, and tool usage
• Build scalable RAG and retrieval pipelines
• Design long-context and multi-modal workflows
• Inference & Model Infrastructure
• Optimize inference pipelines for latency and throughput
• Work with open-source models including Llama, Qwen, Kimi, Mistral, DeepSeek, Gemma, Flux, SDXL, and other frontier/open models

Implement model serving infrastructure using technologies like:

• vLLM
• TensorRT-LLM
• TGI
• Ollama
• SGLang
• Ray Serve
• Build intelligent model routing and fallback systems
• Improve GPU utilization and inference efficiency
• Fine-Tuning & Model Optimization
• Build and manage fine-tuning pipelines
• Work with:
• LoRA / QLoRA
• PEFT
• RLHF/RLAIF concepts
• Quantization
• Distillation
• Evaluate models across latency, quality, and cost tradeoffs

Backend & Platform Engineering

• Develop scalable backend systems using Python
• Design APIs, microservices, async workflows, and distributed systems
• Build production-grade SaaS architecture
• Implement observability, logging, monitoring, and reliability systems
• Work with vector databases, caching systems, queues, and storage layers
• Deployment & Infrastructure
• Deploy AI systems on cloud and GPU infrastructure
• Work with Kubernetes, Docker, and scalable orchestration systems
• Build highly available inference infrastructure
• Optimize infrastructure costs and scalability

Requirements

General requirements

• 3-5 Years in AI architecture and system design
• Strong hands-on Python expertise
• Proven experience building production AI systems
• Experience with LLM inference optimization
• Deep understanding of transformer architectures and modern LLM ecosystems
• Experience with open-source model deployment
• Strong backend engineering experience
• Experience designing scalable SaaS platforms
• Experience with APIs, async systems, and distributed architectures
• Strong debugging and systems-thinking ability

AI/ML Experience

• Multi-agent systems
• RAG architectures
• Fine-tuning pipelines
• Embeddings and vector databases
• Tool-calling frameworks
• Model evaluation and benchmarking
• Prompt orchestration and workflow systems

Infrastructure Experience

• Docker
• Kubernetes
• GPU infrastructure
• CI/CD pipelines
• Cloud platforms (AWS/GCP/Azure)
• Distributed inference systems

What We're Looking For

We are specifically looking for engineers who:

• build things themselves
• move fast
• can go from idea to production
• understand both AI and systems engineering
• can architect and implement
• are comfortable operating in ambiguity
• care about performance and scalability
• are obsessed with execution
• This is not a slide deck architect role.

You should be able to:

• write production code daily
• review system bottlenecks
• optimize inference performance
• debug distributed systems
• build MVPs rapidly
• scale products into production systems
• Bonus Points
• Experience building AI SaaS products from scratch
• Experience with agentic frameworks
• Experience with GPU optimization
• Contributions to open-source AI projects
• Experience with large-scale inference systems
• Startup experience
• Experience working with high-growth engineering teams

If you want to help shape the future of AI infrastructure and build systems that can scale from startup experimentation to enterprise deployments, we'd love to talk.