AI Inference & Serving Model Efficiency

As organisations move Large Language Models (LLMs) from experimentation into production, the demand for specialist AI inference talent continues to grow. DeepRec partners with ambitious startups, scaleups and enterprise organisations to hire engineers building high-performance inference infrastructure, production ML systems and model serving platforms that power real-world AI applications.

Our network spans AI Inference Engineers, LLM Infrastructure Engineers, Model Serving Engineers and AI Performance Engineers with expertise across real-time inference, batch inference, distributed inference and edge inference. We support organisations building scalable inference pipelines, low-latency serving platforms and GPU-accelerated production environments capable of delivering AI at scale.

Whether you’re building inference infrastructure for foundation models, deploying multi-GPU serving environments or improving inference optimisation for production workloads, we connect you with the specialists who make modern AI systems faster, more reliable and more cost-effective.

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Where DeepRec.ai Specialises

Inference, Serving, and Model Efficiency recruitment goals vary widely between organisations, but they share a common focus: running large models reliably and efficiently in production.

DeepRec.ai supports hiring engineers working across areas such as LLM inference, model serving, LLM runtime optimisation, inference optimisation and performance-critical AI infrastructure. This often includes engineers building and maintaining serving stacks using frameworks such as vLLM, NVIDIA Triton Inference Server, TensorRT-LLM, Hugging Face Text Generation Inference (TGI), SGLang, Ray Serve, TorchServe, KServe, BentoML and ONNX Runtime, depending on scale, latency requirements and deployment environments.

Rather than relying on job titles alone, we focus on engineers with ownership of production systems and measurable real-world impact.

Engineers in this space are responsible for improving performance, reducing cost, and ensuring reliability under real-world workloads.

This commonly involves:

Applying optimisation techniques such as quantization (INT8 / FP8), pruning, and distillation
Implementing model compression strategies to reduce memory footprint and inference cost
Leveraging low-level performance improvements such as FlashAttention and FlashDecoding
Tuning inference pipelines for throughput, latency, and hardware efficiency

These decisions are often highly context-dependent, requiring engineers to balance accuracy, speed, cost, and operational complexity.

Technologies & Platforms We Recruit For

The AI inference ecosystem is moving quickly, with organisations adopting specialised frameworks and platforms to deliver faster, more efficient model serving at scale. DeepRec works with businesses building production-ready AI infrastructure, connecting them with engineers experienced in the technologies shaping modern inference systems.

We recruit professionals with expertise across leading model serving frameworks including vLLM, NVIDIA Triton Inference Server, TensorRT-LLM, Ray Serve, TorchServe, KServe, BentoML, ONNX Runtime, Hugging Face Text Generation Inference (TGI), SGLang, LMDeploy, Modal, NVIDIA NIM and llama.cpp.

Beyond deployment frameworks, we support organisations building highly available inference platforms using Kubernetes, GPU orchestration and cloud native infrastructure. Our network includes specialists experienced in distributed model serving, autoscaling inference workloads, multi-GPU deployments, streaming inference architectures and production ML systems designed to meet demanding latency and throughput requirements.

Whether you’re scaling LLM infrastructure for enterprise applications or building new AI products from the ground up, we help organisations secure the engineering talent needed to deliver reliable, high-performance inference in production.

Why Choose DeepRec.ai as Your Talent Partner?

The AI industry often approaches hiring for roles across AI Infrastructure & Distributed Systems as an extension of traditional ML or backend recruitment. Generic job titles are reused, CVs are screened for surface-level familiarity, and critical production experience is assumed rather than validated.

In reality, these roles demand engineers who can operate under real-world constraints, which means balancing latency, throughput, reliability, and cost in production AI systems. Hiring successfully requires context, judgement, and a deep understanding of how these systems behave at scale.

This is where DeepRec.ai adds value. We specialise in identifying engineers who have built, operated, and optimised production AI systems, not just experimented with them. Our experience delivering complex hiring mandates in performance-critical AI environments allows us to assess beyond titles and tooling, focusing instead on real-world capability and impact.

When you partner with DeepRec.ai, you get:

A dedicated delivery team who specialise purely in Inference, Serving & Efficiency across AI infrastructure and distributed systems. This guarantees faster shortlists and higher-confidence hiring decisions.
The niche expertise of a boutique agency, but the resilience and resources of a global brand. We're part of Trinnovo Group, an international staffing business that provides the operational scale, governance, and delivery capability required to support business-critical hiring initiatives.
Adaptable recruitment models to suit your unique business goals. From embedded hiring solutions for high-volume hiring, all the way through to executive search for critical leadership hires.
Access to a global AI engineering community of engaged, qualified, and production-ready engineers.
A consultative, delivery-first approach to recruitment.

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Roles We Recruit For

We support hiring across a range of production-focused AI engineering roles, including:

AI Inference Engineer
Model Serving Engineer
Inference Optimisation Engineer
LLM Infrastructure Engineer
AI Infrastructure Engineer
AI Platform Engineer
ML Systems Engineer
MLOps Engineer
Distributed Systems Engineer
Backend AI Engineer
GPU Software Engineer
GPU Performance Engineer
CUDA Engineer
AI Performance Engineer
Platform Reliability Engineer

Common Use Cases We Support

Inference, serving, and model efficiency hiring is most critical for teams:

Scaling LLM-powered products into production
Operating real-time or low-latency AI systems
Managing high-throughput inference workloads
Optimising infrastructure cost as AI usage grows
Building internal AI platforms or developer tooling

We work with teams where inference performance and system reliability directly affect product quality and commercial outcomes.

FAQ

What makes inference and serving roles difficult to hire for?

These roles require hybrid skill sets across ML, backend engineering, and infrastructure, combined with real-world production experience that is difficult to validate through CVs alone.

Do you recruit for MLOps roles?

Yes. We specialise in MLOps hiring alongside inference, serving, and AI infrastructure roles, supporting teams responsible for deploying, operating, and maintaining production AI systems.

Do you support startup and enterprise hiring?
Yes. We work with startups, scale-ups, and established organisations where production AI systems are business-critical.

Can you support confidential or business-critical hires?
Absolutely. We regularly deliver complex and sensitive hiring mandates where discretion and precision are essential.

Which locations do you service?

We primarily deliver recruitment services across the UK, Ireland, the DACH region, and the United States, where we have deep market knowledge and an established presence. Alongside this, we regularly deliver AI infrastructure, inference, serving, and MLOps hiring mandates on a global basis.