Hire Fine-Tuning Engineersin 2 weeks
BearPlex fine-tuning engineers specialize in adapting open-source and frontier models to your domain (LoRA, QLoRA, full fine-tuning, DPO, RLHF) delivering models that outperform prompt-engineered baselines on your specific tasks while controlling per-call cost at production scale.
What a Fine-Tuning Engineer actually does at BearPlex
A fine-tuning engineer at BearPlex owns the full model adaptation lifecycle: dataset construction (the hardest and most under-appreciated part), evaluation harness design, training run management, hyperparameter sweeps, model evaluation against held-out tasks, and production deployment of the resulting model. They work across the full method stack: LoRA and QLoRA for parameter-efficient adaptation, full fine-tuning when scale justifies it, DPO and ORPO for preference alignment, supervised fine-tuning (SFT) for instruction-following, and continued pre-training for domain shifts. They've shipped fine-tuned models on Hugging Face TGI, vLLM, Together AI, AWS Bedrock custom models, and Azure ML. They know when fine-tuning is the right answer (rigid format compliance, per-call cost optimization at scale, specialized domain language) and when it's the wrong one (fast-changing knowledge that should be in RAG instead). Most importantly, they build evaluation harnesses BEFORE training, because a fine-tuned model with no evaluation is just an expensive version of the base model.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Fine-tuned a Llama 3 70B with QLoRA for a US healthcare client: 12% accuracy lift on clinical entity extraction vs prompted GPT-4 at 1/15th the per-call cost.
Built a DPO-aligned customer support model for a Series C SaaS: replaced GPT-4 in 80% of routing decisions while improving CSAT 4 points.
Fine-tuned a Mistral 7B on 50K labeled support tickets: production deployment serves 200K daily inferences at $400/month vs $11K/month for the prompted GPT-4 baseline.
Led continued pre-training of a 13B-param base model on 50B tokens of domain corpus: improved downstream fine-tuning convergence 3× and final eval scores 8 points.
Skills matrix
The capabilities every BearPlex Fine-Tuning Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| LoRA / QLoRA fine-tuning | Expert | Hugging Face PEFT · Unsloth · LLaMA-Factory |
| Full fine-tuning at scale | Expert | DeepSpeed ZeRO · FSDP · Megatron-LM |
| DPO / ORPO preference alignment | Expert | TRL · Axolotl · OpenRLHF |
| Supervised fine-tuning (SFT) | Expert | TRL · Hugging Face Transformers · Axolotl |
| Dataset construction and curation | Expert | Argilla · Label Studio · custom pipelines |
| Evaluation harness design | Expert | lm-evaluation-harness · OpenAI Evals · custom rubrics |
| Hyperparameter sweeps and tracking | Expert | Weights & Biases · MLflow · Ray Tune |
| Production model serving | Advanced | vLLM · TGI · Triton Inference Server |
| Continued pre-training | Advanced | Megatron-LM · DeepSpeed · custom data pipelines |
| Quantization (GPTQ, AWQ, INT4) | Advanced | AutoGPTQ · AutoAWQ · bitsandbytes |
| Fine-tuned model evaluation against base model | Expert | custom A/B harnesses · MMLU subsets · domain benchmarks |
| GPU cluster management and cost optimization | Advanced | Slurm · Ray · Modal · spot instance orchestration |
How we vet fine-tuning engineers
Technical screen
60-minute deep-dive on a past fine-tuning project. Candidate walks through dataset construction, training infrastructure choices, evaluation methodology, and what they'd do differently. We're looking for engineers who learned from production deployments, not just academic experiments.
Live training exercise
We give the candidate a small dataset and 90 minutes to set up a LoRA fine-tuning run, design an evaluation, and explain their hyperparameter choices. Bonus points for catching that the dataset has known issues we planted (label noise, distribution skew).
Architecture interview
Whiteboard a fine-tuning architecture for a realistic client scenario: domain language model on 100K examples, $2K/month inference budget, weekly retraining cadence. We probe for cost trade-offs, evaluation rigor, and ops awareness.
Reference checks + paid trial
Two engineering reference checks plus a 21-day paid trial on a real client engagement. We don't take engineers off trial until both Hamad and the client engineer report 'I want this person on the team next sprint.'
What clients say
“Their fine-tuning engineer ran the dataset construction with the same rigor we'd apply to a research paper: she rejected 30% of our labeled data after finding leakage. The resulting model was night-and-day better than what we'd been getting with prompts.”
“We thought we needed GPT-4 for everything. Their team showed us a fine-tuned 7B model that hit 94% of GPT-4's accuracy on our task at 1/20th the cost. That single project paid for the engagement 10× over.”
“What separated BearPlex from the other shops we tried: they refused to start training until they had a working evaluation harness. Took an extra week up front and saved us months of debugging downstream.”
Hiring fine-tuning engineers: questions answered
Yes: when the platform supports it. OpenAI fine-tuning (GPT-4o, GPT-3.5-turbo), AWS Bedrock custom models (Claude variants in private preview), Azure OpenAI fine-tuning, Google Vertex AI fine-tuning. Closed-source fine-tuning is more constrained (less control over hyperparameters, longer iteration loop) but sometimes the right answer for clients already on those platforms.
Initial production fine-tuned model: 4-8 weeks for a typical engagement. The training itself is often less than a day; the time goes to dataset construction (weeks), evaluation harness design (1-2 weeks), and iteration based on eval results (weeks). Engagements that try to skip the dataset and evaluation phases reliably ship worse models.
Most production work is in the 7B-13B range (Llama 3.1 8B, Mistral 7B, Qwen 2.5 7B, Phi-3) with LoRA or QLoRA. We fine-tune 70B+ models when client task complexity justifies it (typically agent and reasoning tasks). For closed-source platforms, we fine-tune whatever the platform supports, typically GPT-4o or smaller GPT variants on OpenAI, Claude variants where available on Bedrock.
Yes: for clients with strict data residency requirements (healthcare, financial-services, government), we run training on customer infrastructure (AWS, GCP, Azure account they own; on-prem GPU cluster) rather than third-party fine-tuning APIs. Models stay in customer accounts; we provide the engineering, customer keeps the artifacts.
Both. SFT is the starting point for most engagements. DPO (and increasingly ORPO and KTO) come into play when you have preference data: pairs of 'good' and 'bad' responses from real usage. Full RLHF with reward modeling is rare in client work because the data and infrastructure investment is significant; most clients hit their goals with SFT + DPO.
Always against a held-out evaluation set the model has never seen. We design the evaluation harness BEFORE training, then measure: task accuracy on held-out data, regression on general capabilities (we use MMLU-Pro and a domain-specific subset to catch capability loss), and inference cost/latency. A fine-tuned model that beats the base on the target task but breaks on out-of-distribution inputs is not a win.
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. Time zone overlap with US clients is 5-9 hours; we structure engagements with daily 2-3 hour overlap windows for synchronous work, async handoff for the rest.
Yes. We work with whatever you have: Kubeflow, SageMaker, Vertex AI Pipelines, custom Airflow + Slurm setups, Modal, Ray. If you don't have MLOps infrastructure, we can pair a fine-tuning engineer with one of our MLOps engineers to stand it up alongside the model work.
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