Hire an AI
LLM Engineer
Find an LLM Engineer who goes deeper than prompting — to architectures, fine-tuning, and scale.
LLM Engineers specialize in the architecture, training, fine-tuning, and productionization of large language models. They understand how transformer models work at a deeper level than most AI practitioners — and can apply that knowledge to optimize, customize, and scale LLM systems that most teams treat as black boxes.
- LLM Engineers with training, fine-tuning, and production deployment depth
- Candidates who understand transformer architecture, not just API calls
- We screen for practical depth, not just certifications
- Contract, contract-to-hire, or full-time — your choice
- US and Canada talent. Shortlist in 5–10 business days.
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About CRM Force
Boutique means something different when the person who built the company has done the work.
Saad Ahmed spent 20 years as a CRM consultant, solution architect, and implementation lead — building marketing automation and CRM systems for clients from start-ups to publicly traded enterprises. He founded CRM Force to bring practitioner-led vetting to a space dominated by generalist agencies.
Every candidate goes through a two-step internal qualification process — reviewed by two separate evaluators before they reach you. We confirm experience, certifications, technical depth, and fit. You receive a shortlist, not a resume dump.
CRM consultants, architects, developers, and admins. Marketing automation specialists. RevOps leaders, business analysts, and project managers. If it touches the CRM or marketing technology stack, we place it. Contract, contract-to-hire, and direct hire — across all seniority levels.
We Recruit for You
Three reasons companies choose us over a traditional recruiter — and what makes the difference in practice.
Vetted by Someone Who Has Done the Work
Saad Ahmed, founder of CRM Force, spent 20 years as a CRM Consultant and Solution Architect — building HubSpot implementations, designing data models, and running client engagements himself. When he reviews a candidate for your role, he is not reading a job description. He is evaluating whether someone can do work he has done himself. That distinction matters.
No Resume Dumps. A Shortlist You Can Act On.
You receive 3 to 5 pre-vetted candidates, each with our written assessment — why we believe they fit your role, where they are strong, and what to probe in the interview. Our internal process puts every candidate through two separate evaluation stages before they reach you. You only meet people who have cleared the bar.
Three Engagement Models. One Standard.
Contract for short and long-term engagements. Contract-to-hire when you want to try before you commit. Direct placement for permanent roles. Every model includes the same sourcing, multi-stage screening, skills assessment, and reference validation. The delivery vehicle changes — the standard does not.
Our Recruitment Process
Four steps from brief to shortlist — no surprises, no padding, no wasted interviews.
We Learn Your Requirements
A focused role briefing call — not a generic intake form. We ask about portal size, Hub configuration, seniority level, technical depth required, team structure, and what a genuinely great candidate looks like for your specific situation. The brief matters more than most clients expect.
We Search Our Network
We work our pre-built database of CRM and HubSpot professionals alongside targeted outreach to passive candidates who are not actively applying. The majority of the people we present are not on job boards — they are available to the right opportunity, if the right person asks.
You Receive a Shortlist
Typically 3 to 5 pre-vetted candidates, each with our written assessment — not just a resume. We tell you why we believe each person is right for your specific role, where they are strong, and what you should probe in the interview. You decide who moves forward.
We Support You to Offer
We coordinate interviews, facilitate debriefs, provide market-rate compensation benchmarks, and support you through the offer stage. The search does not end when we send the shortlist — it ends when you have the right person in the role.
Looking for an LLM Engineer?
We place CRM and Marketing Automation professionals across the US and Canada.
Industries We Serve
LLM Engineers are needed wherever organizations need to customize, optimize, or deeply integrate large language models. We work with organizations of all sizes — from growth-stage startups to publicly traded enterprises — across all major industries.
Not sure what you need? We can help you define the role.
One conversation is usually enough. No commitment required.
Signs You Need an LLM Engineer
A few things we hear from clients before they reach out.
- You need to fine-tune a base model on proprietary data for a specific domain
- Generic LLMs are too slow, too expensive, or not accurate enough for your use case
- You need to run models on-premise or on private infrastructure for compliance reasons
- Your LLM applications have quality, safety, or latency problems that standard engineering cannot solve
- You are evaluating whether to use an API model or train your own, and need expert guidance
What to Look For in an LLM Engineer
The best LLM Engineers understand how these models work mechanistically — not just how to use them.
Red Flags to Watch For
- Cannot explain how attention works or what a context window limit means mechanistically
- Has only used API-based LLMs — no experience running or fine-tuning open-source models
- No experience with PEFT methods like LoRA — treats fine-tuning as full model training
- Unfamiliar with inference optimization and cost-per-token implications
Whatever your sector, we know CRM talent.
Tell us about the role and we will get back to you within one business day.
What an LLM Engineer Does
LLM Engineers go deeper than most practitioners — from model internals to production serving.
Fine-Tuning
Adapts pre-trained LLMs to specific domains or tasks using LoRA, instruction tuning, or RLHF on proprietary datasets.
Model Optimization
Reduces inference cost and latency through quantization, pruning, and efficient serving configurations.
Training Infrastructure
Sets up multi-GPU training environments with DeepSpeed or FSDP for large-scale fine-tuning or pre-training.
Inference Engineering
Deploys high-throughput model serving with vLLM, TGI, or TensorRT-LLM for production scale.
Safety & Alignment
Applies RLHF, DPO, and constitutional AI techniques to align model outputs with safety and quality requirements.
Evaluation Design
Builds rigorous evaluation pipelines that measure LLM quality on domain-specific tasks, not just generic benchmarks.
LLM Engineering Stack
LLM Engineers work across a specialized stack that spans model internals to serving infrastructure.
Open Source Models
Base models available for fine-tuning and deployment.
- Meta Llama 3 / 3.1
- Mistral / Mixtral
- Qwen 2.5
- Phi-3 (Microsoft)
Fine-Tuning Tools
Libraries and frameworks for model adaptation.
- Hugging Face PEFT
- Axolotl
- LLaMA Factory
- Unsloth (efficient fine-tuning)
Training Infrastructure
Hardware and framework orchestration for training.
- DeepSpeed / FSDP
- Megatron-LM
- Lambda Labs / RunPod / H100s
- CUDA programming basics
Inference Serving
High-performance model serving in production.
- vLLM
- HuggingFace TGI
- NVIDIA TensorRT-LLM
- llama.cpp (edge)
Frequently Asked Questions
- What is the difference between an LLM Engineer and a Generative AI Engineer?
- LLM Engineers specialize in the internals of large language models — fine-tuning, architecture, training, and inference optimization. Generative AI Engineers focus on building products and applications around LLMs — RAG, agents, integrations, and evaluation. The LLM Engineer understands how the model works; the Generative AI Engineer focuses on how to use it in a product.
- Do I need an LLM Engineer or can a software engineer handle this?
- For simple API integrations, a skilled software engineer often suffices. If you need fine-tuning, on-premise deployment, model optimization, or are running into quality/latency/cost issues with off-the-shelf APIs, you need an LLM Engineer with deeper model expertise.
- How long does it take to hire an LLM Engineer?
- CRM Force typically delivers a shortlist within 7 to 14 business days. LLM Engineering is a highly specialized discipline and genuine practitioners with hands-on training experience are rare.
- Can an LLM Engineer help evaluate whether to use OpenAI vs an open source model?
- Yes — this is one of the most valuable conversations an LLM Engineer can lead. They can evaluate cost per token, latency, quality, compliance requirements, and customization needs to give you a principled recommendation.
Tell Us What You’re Looking For
Share the role, your timeline, and any must-haves. No commitment required.