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. - Junior candidates with 2 years experience are suitable

AI Engineer (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 Engineer 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 develop 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 design and implementation go together.

Responsibilities

- AI Systems & Multi-Agent Software development - 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 ssoftware - 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

- 2 Years in AI engineering - Robust 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 design and implement - are comfortable operating in ambiguity - care about performance and scalability - are obsessed with execution

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.

📌 Junior AI Engineer (Hands-on coder) WFH (Kolkata)
🏢 Qubrid AI
📍 Kolkata