LLM Engineer Job Description Template (June 2026) (Download For FREE)

What does an LLM Engineer do?

The LLM Engineer specializes in working with large language models to build, fine-tune, evaluate, and deploy language-based AI systems. This role focuses on the full lifecycle of LLM-powered applications from model selection and adaptation through to production deployment and ongoing optimization.

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What are the Key Responsibilities of LLM Engineer

Select, evaluate, and benchmark LLMs for specific business use cases.
Fine-tune open-source LLMs using techniques such as LoRA, QLoRA, and RLHF.
Build inference pipelines optimized for latency, throughput, and cost.
Develop robust evaluation frameworks to measure model quality and safety.
Manage model versioning, serving infrastructure, and A/B testing.
Collaborate with product teams to define LLM integration requirements.

What are the Skills and Requirements for an LLM Engineer?

Deep familiarity with transformer architectures and LLM training dynamics.
Hands-on experience with fine-tuning frameworks such as Axolotl, Unsloth, or TRL.
Proficiency in Python, PyTorch, and Hugging Face ecosystems.
Understanding of quantization, distillation, and efficient inference techniques.

What are the KPIs to track for LLM Engineer?

Performance is evaluated on model evaluation scores, inference efficiency, fine-tuning success rates, and the measurable business impact of deployed LLM features.

Evaluation Score

Performance on domain-specific benchmarks before and after fine-tuning.

Inference Efficiency

Tokens per second and cost per 1,000 tokens in production.

Fine-tuning Success Rate

Percentage of fine-tuning runs that meet quality thresholds.

Reports to

Head of AI / Lead ML Engineer

Collaborates with

Generative AI Engineers, Data Scientists, DevOps Engineers, Product Managers

Leads

Are any specific tools or software required for the LLM Engineer role?

Hugging Face
PyTorch
vLLM
Axolotl
Weights & Biases
Python

What is the qualification of LLM Engineer?

Bachelor's or Master's degree in Computer Science or AI; 2+ years of hands-on experience with large language model development and deployment.

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