AI Engineer

About This Opportunity

We are representing a confidential client in the family technology space, based in France. They are seeking an AI Engineer to own and optimize their self-hosted inference infrastructure while also shipping user-facing AI features across multiple consumer applications.

This is a hands-on technical role where you'll run GPU inference workloads on dedicated hardware, build internal AI platforms, and deliver product-facing agents that help families coordinate their daily lives. You'll work across the full stack—from low-level inference optimization to app-layer feature development.

Responsibilities

• Run and optimize self-hosted inference infrastructure: Deploy and tune serving stacks (vLLM, SGLang, TensorRT-LLM) on dedicated GPU hardware for high throughput and low latency
• Drive aggressive performance optimization: Implement tensor parallelism, quantization (FP8, AWQ, GPTQ), KV-cache and prefix caching, continuous batching, speculative decoding, and concurrency tuning
• Manage multi-model serving: Balance internal workloads and latency-sensitive product traffic through multi-LoRA, routing, and request scheduling
• Improve efficiency and observability: Instrument performance metrics, improve GPU utilization, and surface technical tradeoffs to inform decision-making
• Ship AI features and proactive agents: Build in-app agent layers including proactive nudges, smart suggestions, summarization, drafting, scheduling, and task automation
• Build supporting infrastructure: Create tools, memory systems, orchestration, guardrails, and evaluation harnesses integrated with production APIs
• Rapid prototyping: Work collaboratively to quickly test ideas, including building rough UIs when needed, then harden successful features

What We're Looking For

Required qualifications:

• 5+ years shipping production software with meaningful applied AI or ML experience
• Demonstrated experience running and optimizing self-hosted LLMs on dedicated multi-GPU hardware
• Hands-on expertise with serving stacks such as vLLM, SGLang, or TensorRT-LLM
• Deep knowledge of inference optimization techniques: tensor parallelism, quantization, batching strategies, and KV cache management
• Proven track record optimizing latency, throughput, and GPU utilization
• Strong Python engineering skills with full-stack capabilities
• Experience with agent frameworks (Claude Agent SDK, LangGraph, or similar), LLM APIs, embeddings, and RAG
• Proficiency with AWS, Docker, CI/CD, monitoring, and observability tools
• Experience building internal platforms and tooling that other teams depend on

Ideal candidate attributes:

• Full-stack mindset—you want to work on app-layer features, not just infrastructure
• Performance-focused approach treating latency and efficiency as core engineering concerns
• Comfortable with rapid prototyping and modern AI-first development tools
• Motivated by building technology that makes a meaningful difference in people's lives
• Bonus: Experience with Slack apps, MCP, or agent orchestration at team scale

How We Work With You

When you apply through Get A Job.ai, our talent team will review your profile and conduct an initial screening. If there's a strong match, we'll submit your candidacy directly to our client and guide you through their interview process.

Please apply exclusively through Get A Job.ai—do not contact the client directly, as this is a confidential search.

Pay

Compensation details will be discussed during the screening process based on experience and qualifications.

Equal Opportunity: Get A Job.ai is committed to inclusive recruiting practices. We welcome candidates from all backgrounds and work to ensure fair consideration throughout the hiring process.