Hire an AI
AI Data Engineer
Find an AI Data Engineer who builds the data infrastructure that makes AI systems reliable.
AI Data Engineers build and maintain the data pipelines, feature stores, and data infrastructure that AI systems depend on. Without reliable, high-quality data infrastructure, the best ML models fail in production. AI Data Engineers bridge traditional data engineering with the specific demands of machine learning — including point-in-time correctness, feature versioning, and training/serving consistency.
- AI Data Engineers who understand ML-specific data requirements, not just general ETL
- Candidates screened for feature store, pipeline, and data quality 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 an AI Data Engineer?
We place CRM and Marketing Automation professionals across the US and Canada.
Industries We Serve
AI Data Engineers are critical for any organization building machine learning systems that depend on consistent, high-quality data at scale. 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 AI Data Engineer
A few things we hear from clients before they reach out.
- Your ML models perform well in training but degrade in production due to data inconsistencies
- Different teams are computing the same features differently, causing training/serving skew
- Your data pipelines are not reproducible and historical data is unavailable for retraining
- Data quality issues are the leading cause of your AI project failures
- You are scaling from a few models to dozens and need centralized, governed feature infrastructure
What to Look For in an AI Data Engineer
AI Data Engineers understand what makes data infrastructure different when machine learning is the consumer.
Red Flags to Watch For
- Only has traditional ETL experience — no understanding of ML-specific data requirements like point-in-time correctness
- Cannot explain training/serving skew and how they have prevented it
- No experience with feature stores or feature versioning for reproducible model training
- Treats data quality as the data team's problem, not an ML infrastructure responsibility
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 AI Data Engineer Does
AI Data Engineers build the data foundation that ML models are trained on and served from.
Feature Pipeline Design
Builds pipelines that compute and store ML features consistently for both training data generation and real-time serving.
Feature Store Management
Implements and maintains centralized feature stores that give ML teams reliable, versioned access to training and serving features.
Training Data Pipelines
Automates the extraction, transformation, and loading of training datasets — with point-in-time correctness and reproducibility.
Data Quality Automation
Builds systematic data validation checks that catch quality issues before they propagate to model training or inference.
Real-Time Data
Designs streaming data infrastructure for ML systems that need low-latency feature computation for online inference.
Data Infrastructure
Manages the underlying data platform — cloud storage, compute, and orchestration — that all AI data workflows run on.
AI Data Engineering Stack
AI Data Engineers work across a specialized data infrastructure stack.
Feature Stores
Centralized feature management for ML.
- Feast (open source)
- Tecton (managed)
- Hopsworks
- Databricks Feature Store
Data Platforms
Cloud data warehouse and lakehouse platforms.
- Databricks (Delta Lake)
- Snowflake
- BigQuery
- Apache Iceberg
Pipeline Orchestration
Tools for scheduling and managing data workflows.
- Apache Airflow
- Prefect
- dbt (transformations)
- Dagster
Quality & Observability
Systems for data quality and monitoring.
- Great Expectations
- Monte Carlo
- dbt tests
- Custom alert frameworks
Frequently Asked Questions
- What is the difference between an AI Data Engineer and a regular Data Engineer?
- A regular Data Engineer builds pipelines for analytics and reporting — moving data from sources to warehouses for BI tools. An AI Data Engineer has the additional expertise to build feature stores, handle point-in-time correctness, manage training data pipelines, and understand the specific data quality requirements that ML models impose.
- Do I need an AI Data Engineer or an MLOps Engineer?
- They are complementary. An AI Data Engineer owns the data layer — getting the right data to models in the right form. An MLOps Engineer owns the model layer — training pipelines, deployment, and monitoring. For early-stage ML teams, one person often covers both. At scale, they are distinct roles.
- How long does it take to hire an AI Data Engineer?
- CRM Force typically delivers a shortlist within 5 to 10 business days. AI Data Engineers with both data engineering depth and ML-specific expertise (feature stores, point-in-time correctness) are more specialized than general data engineers.
- What industries hire AI Data Engineers?
- Any industry with mature ML programs: fintech, e-commerce, healthcare tech, media and streaming, logistics, and enterprise SaaS. AI Data Engineers are needed wherever data pipelines feed production ML models.
Tell Us What You’re Looking For
Share the role, your timeline, and any must-haves. No commitment required.