Hire an AI
MLOps Engineer
Find an MLOps Engineer who automates the full ML lifecycle — training to production.
MLOps Engineers build the infrastructure and automation that lets ML teams ship models faster, more reliably, and at scale. They bring software engineering discipline to machine learning — turning ad-hoc notebook workflows into reproducible, monitored, CI/CD-driven pipelines that the whole organization can depend on.
- MLOps Engineers who have built production ML pipelines, not just theoretical frameworks
- Candidates screened across training pipelines, model serving, and drift monitoring
- 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 MLOps Engineer?
We place CRM and Marketing Automation professionals across the US and Canada.
Industries We Serve
MLOps Engineers are critical for any organization that has moved from ML experiments to running multiple models in production. 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 MLOps Engineer
A few things we hear from clients before they reach out.
- Your ML models take weeks to retrain and redeploy — it should take hours
- Different data scientists are using different tools with no standardization
- Models degrade in production and no one is monitoring them systematically
- Your team cannot reproduce last quarter's model training run
- You are scaling from 1 model to 10+ and the ad-hoc process is breaking down
What to Look For in an MLOps Engineer
Strong MLOps Engineers are software engineers first who understand what makes ML different from regular software.
Red Flags to Watch For
- Has never built a training pipeline that runs without manual intervention
- Cannot explain the difference between data drift and concept drift
- Only has DevOps experience — no familiarity with ML-specific challenges like reproducibility and feature leakage
- Has not worked with feature stores or dealt with training/serving skew
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 MLOps Engineer Does
MLOps Engineers build the automation layer that keeps ML systems healthy and productive.
Training Pipelines
Builds automated, reproducible training pipelines that run on schedule or on trigger, with full experiment tracking.
Model Deployment
Automates model deployment to serving infrastructure — from containerized REST APIs to streaming inference.
Model Monitoring
Sets up drift detection, performance monitoring, and alerting so model degradation is caught before it affects users.
Feature Store
Builds and maintains centralized feature stores with point-in-time correctness for consistent training and serving.
ML CI/CD
Implements continuous integration and delivery for ML — automated testing, validation, and deployment gates.
Infrastructure Design
Designs and manages the compute, storage, and networking infrastructure that ML workflows run on.
MLOps Stack
MLOps Engineers work across a layered stack of tools and platforms.
Training & Tracking
Experiment management and training automation.
- MLflow
- Weights & Biases
- Kubeflow Pipelines
- DVC (Data Version Control)
Serving & Deployment
How models reach production reliably.
- BentoML / Seldon
- Triton Inference Server
- SageMaker Endpoints
- FastAPI model wrappers
Orchestration
Workflow scheduling and pipeline management.
- Apache Airflow
- Prefect
- Metaflow
- Argo Workflows
Monitoring & Ops
Keeping production models healthy.
- Evidently AI
- WhyLabs
- Prometheus + Grafana
- Great Expectations
Frequently Asked Questions
- What is the difference between an MLOps Engineer and a DevOps Engineer?
- DevOps Engineers build CI/CD pipelines and infrastructure for software. MLOps Engineers do the same for machine learning systems, with the added complexity of training pipelines, feature stores, model registries, and drift monitoring. ML systems have unique challenges — like reproducibility and training/serving skew — that require ML-specific expertise.
- Do I need an MLOps Engineer or a Machine Learning Engineer?
- If you are still building your first models, you need an ML Engineer. If you have models in production and the process of training, deploying, and monitoring them is becoming a bottleneck, you need an MLOps Engineer.
- How long does it take to hire an MLOps Engineer?
- CRM Force typically delivers a shortlist within 5 to 10 business days. Senior MLOps Engineers with multi-cloud and large-scale pipeline experience may take slightly longer.
- Can an MLOps Engineer also do data engineering?
- Some can, but the roles are distinct. Data Engineers focus on data pipelines for analytics and warehousing. MLOps Engineers focus on feature pipelines and model serving infrastructure. Many organizations need both; some early-stage companies find one person who covers both adequately.
Tell Us What You’re Looking For
Share the role, your timeline, and any must-haves. No commitment required.