Hire an AI
Generative AI Engineer
Find a Generative AI Engineer who builds LLM-powered products — not just prompts.
Generative AI Engineers build production systems around large language models — RAG pipelines, AI agents, fine-tuned models, and multi-modal applications. They bring the engineering discipline that turns a demo into a reliable, scalable product, handling the edge cases, latency, cost, and safety challenges that make generative AI production-ready.
- Engineers with production LLM applications — not just API experiments
- Candidates screened for RAG, agentic, and fine-tuning depth
- 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 a Generative AI Engineer?
We place CRM and Marketing Automation professionals across the US and Canada.
Industries We Serve
Generative AI Engineers are needed wherever companies are building AI-powered products on top of foundation 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 a Generative AI Engineer
A few things we hear from clients before they reach out.
- You have a proof-of-concept LLM application that needs to become a real product
- Your team can prompt GPT but cannot handle hallucinations, latency, or cost at scale
- You need a RAG system that retrieves reliably from your proprietary knowledge base
- You are building an AI agent or multi-step AI workflow
- You need fine-tuning or instruction tuning on a base model for a specific domain
What to Look For in a Generative AI Engineer
The best Generative AI Engineers go beyond prompt engineering — they architect systems that are reliable in production.
Red Flags to Watch For
- Has only used ChatGPT or basic API calls — no production systems to reference
- Cannot describe how they handle hallucinations in a RAG system
- No experience with evaluation beyond "it looked good in testing"
- Unfamiliar with token costs, context window limits, or latency budgets
Whatever your sector, we know CRM talent.
Tell us about the role and we will get back to you within one business day.
What a Generative AI Engineer Does
Generative AI Engineers build the infrastructure and applications that make foundation models useful in production.
RAG Pipeline Design
Builds retrieval-augmented generation systems that give LLMs accurate, grounded access to proprietary knowledge.
LLM Agent Development
Creates autonomous AI agents that can use tools, call APIs, search the web, and execute multi-step tasks.
Fine-Tuning
Adapts foundation models to specific domains and tasks using LoRA, instruction tuning, or RLHF.
Prompt Engineering
Designs robust prompt templates with few-shot examples, chain-of-thought patterns, and structured output handling.
Evaluation Pipelines
Builds automated evaluation systems to measure generation quality, factual accuracy, and regression over time.
API & Integration
Integrates LLM capabilities into existing applications via REST APIs, SDKs, and streaming endpoints.
Generative AI Engineering Stack
Generative AI Engineers work across the full stack of tools, models, and infrastructure.
Foundation Models
The core language models at the heart of generative applications.
- OpenAI GPT-4o
- Anthropic Claude 3.5
- Google Gemini
- Meta Llama 3 (open source)
RAG & Retrieval
Infrastructure for grounding LLMs in private knowledge.
- Vector databases (Pinecone, Chroma)
- Embedding models
- Re-ranking (Cohere, cross-encoders)
- Hybrid search (BM25 + semantic)
Agentic Frameworks
Tools for building multi-step AI workflows.
- LangChain / LangGraph
- LlamaIndex
- CrewAI / AutoGen
- Custom tool-calling pipelines
Eval & Guardrails
Ensuring generative outputs are safe and accurate.
- RAGAS evaluation framework
- LLM-as-judge pipelines
- Guardrails AI
- Prompt injection defenses
Frequently Asked Questions
- What is the difference between a Generative AI Engineer and a Prompt Engineer?
- A Prompt Engineer focuses primarily on writing and optimizing prompts. A Generative AI Engineer builds the entire system around LLMs — RAG pipelines, agent frameworks, evaluation, fine-tuning, and production deployment. The engineering scope is much broader.
- Do I need a Generative AI Engineer if I already have software engineers?
- Possibly not, if the integration is simple. But for production-grade LLM applications — with RAG, agents, fine-tuning, evaluation, and cost optimization — dedicated Generative AI Engineering expertise significantly reduces risk and time to value.
- How long does it take to hire a Generative AI Engineer?
- CRM Force typically delivers a shortlist within 5 to 10 business days. Generative AI is a rapidly evolving field and genuine production experience (not just demo projects) is rare — we focus on practitioners who have shipped.
- What is a typical engagement for a Generative AI Engineer?
- Common engagements include: building a RAG system over internal documents, adding an AI assistant to a product, fine-tuning a base model for a specific domain, or setting up LLM evaluation infrastructure.
Tell Us What You’re Looking For
Share the role, your timeline, and any must-haves. No commitment required.