Junior AI 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.

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

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 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.

Seniority level: Entry level

Employment type: Full-time

Industries: Software Development