AI Infrastructure & Experience Engineer

Inference Optimization: Deploy and tune multiple LLMs and generative multimodal models on local inference hardware. Optimize performance metrics (TTFT, tokens/sec) via model quantization, caching strategies, and architecture-specific adjustments.

Systems Engineering & CUDA: Leverage deep knowledge of the CUDA environment to build custom kernels, ensuring maximum utilization of the low cost GPU compute.

Orchestration & Integration: Seamlessly bridge inference backends with orchestration layers (LiteLLM, Ollama, etc.) and frontends like OpenWebUI.

Rapid Prototyping: Build functional, high-fidelity demos showcasing model memory capabilities, agentic workflows, and context-aware web search.

Peripheral Connectivity: Implement communication protocols to bridge local AI compute with peripheral devices, including smart TVs, household appliances, and XR hardware.

Skills: Technical Qualifications

Recent experience in model optimization required Hardware & Compute: Proven experience with NVIDIA eco-systems and ARM64 architecture.

Systems Programming: Advanced proficiency in C++, Python, and Rust. Deep familiarity with CUDA and the ability to author/debug custom CUDA kernels for compute-intensive tasks.

AI/ML Frameworks: Extensive experience with modern inference engines (llama.cpp, TensorRT-LLM, Ollama) and orchestration frameworks (LiteLLM).

Software Engineering: Robust understanding of asynchronous programming (FastAPI), containerization (Docker/Kubernetes), sandbox environments, and API design for low-latency communication.

Full-Stack Prototyping: Ability to quickly spin up modern frontend UIs (React, Next.js, or similar) to present AI-driven intelligence to end users.

Communication Protocols: Familiarity with WebSockets, gRPC, and REST for device-to-device communication in a local network environment.

Keywords:

Education: Ideal Candidate Profile

The "Builder" Mindset: You are energized by the prospect of building proofs-of-concept in days rather than months. You thrive in environments where speed and creativity are paramount.

Problem Solver: You approach unsolved, messy engineering challenges with enthusiasm rather than trepidation.

Architectural Vision: You see the "big picture" of how AI becomes part of the consumer's daily life, not just how the model generates text.

Agile & Adaptable: You are comfortable working in a fast-paced environment where priorities shift based on the results of rapid experimentation.

Degree in Computer Science, Machine Learning or Artificial Intelligence Specialization preferred, but not required

3 years of relevant industry experience required

Skills and Experience:

Required Skills:

INFERENCE OPTIMIZATION

NVIDIA ECOSYSTEMS

CUSTOM CUDA KERNEL DEVELOPMENT

ARM64 ARCHITECTURE, API DESIGN

LOW-LATENCY COMMUNICATION

FRONTEND UI DEVELOPMENT

REACT

NEXT.JS

WEBSOCKETS

GRPC

REST

DEVICE-TO-DEVICE COMMUNICATION

PROBLEM SOLVING

ARCHITECTURAL VISION

AGILITY

ADAPTABILITY