Hire an LLM Engineer
in 6 days.
LLM Engineers work below the API layer. They fine-tune models for domain-specific accuracy, build evaluation frameworks that measure whether the AI is actually improving, and design the training pipelines that turn general-purpose language models into specialized business tools.
What a LLM Engineer does
LLM Engineers operate at the model layer — below the prompt engineering surface and above the infrastructure. They take foundation models and adapt them for specific business domains through fine-tuning, distillation, and alignment techniques. When a general-purpose model produces 80% accuracy on a domain-specific task and the business needs 95%, the LLM Engineer closes that gap.
The work involves dataset curation (assembling and cleaning the training data that teaches the model domain-specific behavior), fine-tuning strategy selection (full fine-tuning vs. LoRA vs. QLoRA depending on model size and compute budget), training pipeline implementation (distributed training across GPUs, checkpointing, hyperparameter optimization), and evaluation framework design (building the benchmarks that prove the fine-tuned model outperforms the base model on the metrics that matter to the business).
LLM Engineers also handle the increasingly important work of alignment — ensuring models follow instructions consistently, refuse harmful outputs, and maintain quality across edge cases. This involves techniques like RLHF (reinforcement learning from human feedback) or DPO (direct preference optimization), which require both engineering skill and an understanding of how human evaluators should be instructed to judge model outputs.
Why this role is hard to fill right now
Fine-tuning production language models requires a rare combination of deep learning engineering, distributed systems knowledge, and practical experience with GPU compute economics. The skill set emerged from research labs and is only now transitioning into commercial engineering roles. Most candidates who claim LLM engineering experience have fine-tuned small models on single GPUs as learning exercises. Production fine-tuning at enterprise scale — multi-GPU training runs costing thousands of dollars per iteration, evaluation suites with hundreds of test cases, deployment to serving infrastructure — is a meaningfully different skillset.
How Xylity fills this role
We evaluate LLM Engineers on the specifics of their training runs: what models they fine-tuned, on what data, using which techniques, and what measurable improvement they achieved. We assess their understanding of compute economics (can they estimate the cost of a training run before starting it?) and their evaluation methodology (how do they know the fine-tuned model is better, and better at what?). We also verify experience with model serving and inference optimization, because a fine-tuned model that can't serve at production latency requirements is not a finished product.
Where this role gets deployed
Domain-specific fine-tuning
Adapting a foundation model for legal, medical, financial, or technical domain accuracy using curated enterprise training data.
Model evaluation pipeline
Building automated evaluation suites that benchmark model performance against domain-specific metrics and regression tests.
Inference optimization
Deploying fine-tuned models to production serving infrastructure with latency, throughput, and cost targets.
What to look for when interviewing
These are the dimensions our consultants evaluate when screening LLM Engineer candidates. Use them as a guide during your own interviews.
Training experience
Have they run fine-tuning jobs on models larger than 7B parameters with real business data?
Evaluation rigor
Can they describe their evaluation methodology beyond "it looks better"?
Compute economics
Do they understand GPU cost estimation and training run budgeting?
Deployment
Have they deployed fine-tuned models to production serving infrastructure?
Request LLM Engineer Profiles
Tell us about your project context and timeline. We'll deliver 2–4 curated, pre-vetted profiles within 6 days of your initial brief.