Agentic AI​/Multi-Agent Systems Engineer

Agentic AI / Multi-Agent Systems EngineerML Engineer I

Who We Are:

Born digital, UST transforms lives through the power of technology. We walk alongside our clients and partners, embedding innovation and agility into everything they do. We help them create transformative experiences and human-centered solutions for a better world.

UST is a mission-driven group of 29,000+ practical problem solvers and creative thinkers in more than 30 countries. Our entrepreneurial teams are empowered to innovate, act nimbly, and create a lasting and sustainable impact for our clients, their customers, and the communities in which we live.

With us, you'll create a boundless impact that transforms your career-and the lives of people across the world.

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UST is searching for an Agentic AI / Multi-Agent Systems Engineer who will develop and optimize multi-agent AI systems and agentic workloads.

• Work with agent frameworks and orchestration technologies to design scalable solutions
• Select, evaluate, and deploy appropriate models and inference engines based on workload requirements
• Debug and optimize performance across the full stack: agent model inference engine hardware
• Benchmark and profile AI workloads to identify and resolve performance bottlenecks
• Optimize model execution for latency, throughput, memory efficiency and accelerator utilization
• Collaborate on the complete execution path from agent logic through GPU hardware

This position description identifies the responsibilities and tasks typically associated with the performance of the position. Other relevant essential functions may be required.

• Bachelor's degree in Electrical Engineering or Computer Engineering
• 2-4 years of platform debug experience with BIOS/FW/platform ingredients
• Required Experience & Knowledge
• Linux & Systems
• Strong Linux development and debugging skills
• Understanding of processes, threads, memory, PCIe, DMA, kernel modules, and device drivers
• Ability to debug workloads across the application runtime driver stack
• Agentic AI & Multi-Agent Systems
• Hands‑on development of multi‑agent workloads
• Experience with agent orchestration, tool/function calling, memory planning and agent-to-agent communication
• Experience with agent frameworks such as Lang Graph/Lang Chain, Auto Gen, or CrewAI
• Models & Inference
• Strong understanding of LLMs, SLMs and multimodal models
• Knowledge of Transformer architecture, attention, tokenization, context windows, and KV cache
• Hands‑on experience with model selection, evaluation, and deployment
• Understanding of model formats and optimization: ONNX, Open VINO IR, safe tensors, quantization (FP16/BF16/INT8/INT4)
• Experience with inference engines:
  Open VINO, ONNX Runtime, vLLM, llama.cpp, or TGI
• Understanding of prefill vs. decode, batching, continuous batching, speculative decoding, and KV-cache management
• Ability to optimize for latency, throughput, tokens/sec, memory footprint, and accelerator utilization
• Accelerator & Compute Stack
• Understanding of the complete execution path:
  Agent Model Inference Engine Middleware Compute Runtime GPU Driver Hardware
• Understanding of GPU memory, kernel execution, synchronization, device selection and host/device data movement
• Familiarity with middleware/accelerator compute runtimes: OMZ/OneAPI/SYCL, Level Zero, or OpenCL
• Programming & Development
• Strong Python skills
• Working knowledge of C/C++
• Git/Git Hub and Linux shell proficiency
• Debugging tools expertise
• Practical use of Git Hub Copilot for development, debugging and code generation
• Docker/container fundamentals
• Performance Engineering
• Ability to benchmark and profile AI workloads
• Understanding of latency, throughput, tokens/sec, GPU utilization, memory bandwidth and CPU/GPU bottlenecks
• Ability to identify performance issues at different stack levels
• Desired Skills
• Intel GPU architecture and Linux GPU driver stack
• DRM/i915 and newer Intel GPU driver architecture
• Intel GPU profiling and telemetry
• Speculative decoding and continuous batching experience
• Tensor/pipeline parallelism knowledge
• Model conversion, graph optimization and operator/kernel fusion
• MCP (Model Context Protocol)
• Advanced RAG architectures
• Agent observability/tracing
• Mo…