Skip to content

Senior / Staff (8+ Years Experience)

Hire Open-Source LLM Fine-Tuning & Self-Hosting Engineers

Hire MLOps specialists who self-host and fine-tune open-source LLMs (Llama 3, DeepSeek, Mistral) on dedicated GPUs, slashing API costs.

Technical Craft

Core Competencies & Architectural Rigor

What sets our senior senior llm fine-tuning & mlops engineer talent apart: deep domain fundamentals, zero-rework architecture, and proven delivery track records.

01

LoRA / QLoRA Parameter-Efficient Fine-Tuning

Training open-source models on domain-specific syntax, medical nomenclature, and JSON schemas.

02

High-Throughput Model Serving (vLLM)

Maximizing GPU memory bandwidth with PagedAttention, continuous batching, and tensor parallelism.

03

GPU FinOps & Cloud Infrastructure

Provisioning dedicated cloud GPU instances (A100/H100) to replace expensive commercial API token bills.

Proven Delivery

What Our Senior LLM Fine-Tuning & MLOps Engineer Ships

Our engineers take end-to-end responsibility for critical milestones, unblocking your roadmap without requiring micro-management.

Self-hosted Llama 3 70B cluster handling 50M daily tokens inside private VPC

Production-ready, tested, and documented implementation.

Fine-tuned lightweight 8B model matching GPT-4o on structured JSON extraction

Production-ready, tested, and documented implementation.

Private enterprise LLM deployment satisfying strict HIPAA/GDPR data residency

Production-ready, tested, and documented implementation.

OpenAI-compatible drop-in API proxy backed by dedicated GPU compute

Production-ready, tested, and documented implementation.

Technology Stack & Tooling Mastery

Primary frameworks, languages, and cloud systems utilized in production:

vLLMLlama 3DeepSeekPyTorchLoRA / QLoRANVIDIA CUDA
Related Service Offering

AI development

LLM systems that survive compliance review: schema-validated extraction, human-in-the-loop workflows, and audit trails — measured in cycle time, not demos.

Explore AI development

Frequently Asked Questions

Questions About Hiring a Senior LLM Fine-Tuning & MLOps Engineer

When should a company fine-tune instead of using RAG?

Use RAG to supply factual dynamic knowledge. Use fine-tuning to teach models specialized jargon, rigid output formats, or proprietary coding syntax.

How much money can self-hosting LLMs save?

At volumes exceeding 25–50M tokens per day, self-hosting dedicated GPU instances with vLLM typically reduces operational costs by 60–85%.

Ready to add a senior senior llm fine-tuning & mlops engineer to your team?

Schedule a 30-minute discovery call. We review your requirements and deploy senior engineers within 14 days.