LLM Engineer – Core AI

Role Overview

This role is intended for an AI/LLM engineer who can take existing Large Language Models and turn them into reliable, scalable, and production-ready AI infrastructure for Gersuz.

The LLM Engineer will be responsible for the technical core of the Gersuz AI system, including model deployment, inference, optimization, fine-tuning, AI APIs, and model evaluation.

This position focuses on the AI engine behind Gersuz products and AI Agents rather than the design or implementation of business-specific AI Agents.

Strategic Mission

You will be responsible for developing and maintaining the core intelligence layer of the Gersuz platform.

Your role will be to evaluate available LLMs, deploy suitable models on our infrastructure, adapt them to Gersuz use cases, optimize their performance, and provide reliable AI services through well-designed APIs.

You will work closely with the product, SaaS, backend, and AI teams to make the Gersuz AI Core available as a stable foundation for different products and AI Agents.

Key Responsibilities

• Evaluate and select suitable existing LLMs for different Gersuz use cases
• Deploy and operate LLMs on GPU/server infrastructure
• Integrate open-source and commercial AI models into the Gersuz AI Core
• Build and maintain production-ready LLM inference services
• Develop AI APIs for communication between the AI Core and Gersuz applications
• Implement model loading, serving, versioning, and configuration
• Optimize model performance, latency, memory usage, and infrastructure costs
• Work with quantization and model optimization techniques when appropriate
• Prepare datasets for model adaptation and training
• Perform fine-tuning using techniques such as LoRA and QLoRA
• Experiment with different models and configurations to improve task-specific performance
• Implement embeddings and AI-related supporting services when required
• Support RAG and knowledge-based AI capabilities
• Develop evaluation methods for measuring model quality and reliability
• Monitor AI model performance in production
• Troubleshoot model, inference, GPU, and AI service issues
• Maintain documentation for models, configurations, APIs, and AI infrastructure
• Collaborate with the product and development teams to translate AI requirements into practical technical solutions

Required Expertise

• Large Language Models (LLMs)
• Python
• PyTorch
• Hugging Face Transformers
• LLM inference and model serving
• AI model deployment
• REST API development
• FastAPI or similar Python API frameworks
• Linux
• Docker
• Git
• Basic GPU and CUDA concepts
• Understanding of model architecture and transformer-based models
• Understanding of AI model evaluation and performance optimization

Strongly Preferred Skills

• Hugging Face ecosystem
• vLLM
• LoRA / QLoRA
• PEFT
• Model quantization
• bitsandbytes
• Embeddings
• RAG
• Vector databases
• Synthetic data generation
• Dataset preparation
• ML experiment tracking
• GPU optimization
• Multi-model inference environments

Ideal Professional Experience

• 2–3 years of practical experience in software engineering, machine learning, NLP, or AI development
• Hands-on experience working with LLMs beyond basic API usage
• Experience deploying AI models on local or cloud GPU infrastructure
• Experience integrating and serving open-source LLMs
• Experience with fine-tuning or adapting existing language models
• Experience developing production APIs for AI services
• Experience optimizing model inference and resource usage
• Experience working with AI products or production software systems
• Ability to independently investigate new models, frameworks, and AI technologies

Preferred Professional Profile

We are particularly interested in engineers who have built real AI systems rather than only completed academic or tutorial-based projects.

Experience with self-hosted LLMs, model deployment, fine-tuning, inference optimization, or production AI APIs is highly valuable.

The typical target profile is an early-career to mid-level engineer with strong practical skills and the ability to take ownership of the AI Core.

Relevant Academic Background

Preferred Fields Include

• Computer Science
• Artificial Intelligence
• Machine Learning
• Software Engineering
• Data Science
• Computer Engineering
• Information Technology
• Natural Language Processing

Valuable Certifications

⚠️Formal certifications are not required. Practical experience and demonstrable AI engineering work are more important.

Relevant certifications in areas such as:

• Machine Learning
• Deep Learning
• Cloud AI / Machine Learning
• NVIDIA CUDA
• Cloud Computing

may be considered an advantage.

⚠️Important Note

This position is focused on the Core AI infrastructure of Gersuz, not on developing individual business AI Agents.

The LLM Engineer will provide the underlying intelligence layer, models, inference services, APIs, training capabilities, and optimization required by the rest of the Gersuz ecosystem.

The AI Agent layer, business workflows, product logic, and application-level integrations will be developed collaboratively by the wider Gersuz development and product teams.

The ideal candidate should therefore be comfortable working with existing AI models and turning them into reliable production systems rather than focusing on training large language models from scratch.

We value practical engineering ability, experimentation, problem-solving, and the ability to learn new AI technologies quickly more than academic credentials alone.