AI Inference Platform Engineer (Go) – Open-Weight Models

Intelligent Inference; Islamabad; On-site; PKR 2,790,000 - 5,580,000; Full time

Benefits offered by this job

Equity available

Job summary

Intelligent Inference is seeking an AI Engineer for its i2 platform to own the path from API edge to accelerator and back. You will operate the Go gateway and serving engines, deploying open-weight models on local hardware and tuning performance for enterprise deployments.

You will need strong Go production experience, hands-on LLM serving knowledge, and a solid mental model of transformer inference. Linux, containers, and Python are essential, with a focus on measurement and verifiable metrics.

Qualifications

• Proficient Go production services engineering.
• Hands-on experience serving LLMs in production.
• Strong understanding of transformer inference concepts.
• Linux, containers, and hardware topology expertise.
• Python for model/evaluation work.
• Ability to measure and verify performance metrics.

Responsibilities

• Extend and operate the Go API gateway and routing logic.
• Deploy and tune open-weight models on Ascend/NPU clusters and other stacks.
• Own serving performance: batching, cache, parallel layouts, decode behaviour.
• Handle quantisation work from BF16 to INT8/INT4 and assess quality impact.
• Work within an 8-chip HCCS coherent domain per cluster and MoE routing constraints.
• Build and operate retrieval infrastructure including vector stores and embeddings.
• Instrument all metrics: latency, tokens/second, cost per million tokens.

Skills

Go

LLM serving

Linux

Python

Measurement/telemetry

Tools

Kubernetes

vLLM

SGLang

TensorRT-LLM

MindIE

CANN

Job description

Intelligent Inference is seeking an AI Engineer for its i2 platform to own the path from API edge to accelerator and back. You will operate the Go gateway and serving engines, deploying open-weight models on local hardware and tuning performance for enterprise deployments.

You will need strong Go production experience, hands-on LLM serving knowledge, and a solid mental model of transformer inference. Linux, containers, and Python are essential, with a focus on measurement and verifiable metrics.