Overview

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.

Responsibilities

• 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.
• 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
• 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

Qualifications

• 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
• Multi-agent systems
• RAG architectures
• Fine-tuning pipelines
• Embeddings and vector databases
• Tool-calling frameworks
• Model evaluation and benchmarking
• Prompt orchestration and workflow systems
• Docker
• Kubernetes
• GPU infrastructure
• CI/CD pipelines
• Cloud platforms (AWS/GCP/Azure)
• Distributed inference systems
• 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