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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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.