Hire MLOps Engineers
Many teams build models that never leave a notebook, because nobody owns deployment, monitoring, or retraining. Pipelines break the moment they hit production. Kodexo Labs supplies embedded MLOps engineers who join your team and keep machine learning models running reliably once in production.
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TRUSTED BY ENTERPRISES
Each engineer you hire through our staff augmentation model owns a defined slice of the machine learning lifecycle. Here are the 6 core areas your embedded MLOps hire covers from their first week.
Our Core Capabilities
Getting trained models live, served reliably through deployment and serving.
Shipping model updates safely and fast, automated through CI/CD pipelines.
Catching model drift before your users do, through live monitoring.
Controlling compute cost and capacity through infrastructure and GPU management.
Keeping production AI agents dependable through LLMOps and agentic operations.
Keeping predictions accurate over time through model versioning and retraining.
IN THE NEWS
AI-powered products across 25+ industries
Earned Top-Rated Reviews on Clutch
Client retention across long-term engagements
Expert Team global offices across the US, UK, Canada
What Your Embedded MLOps Engineer Owns
Production machine learning fails in predictable places: deployment, delivery, monitoring, infrastructure, agent operations, and retraining. Your embedded engineer takes ownership across all 6, so models stay reliable long after the first version ships live to users.
Model Deployment & Serving
Trained models sit unused when nobody can serve them at scale. Your engineer packages, deploys, and exposes them as stable, callable production endpoints.
puts each model behind a stable API so your apps can call it live.
adds or drops copies fast as load grows so calls stay quick, not slow.
Put an MLOps Engineer on Your Team Now
Ship your models faster and keep pipelines reliable from the first sprint. Bring in an engineer who owns deployment, monitoring, and ongoing retraining.
Proof From Production Systems
Diesel Laptops
Fleet technicians lost minutes on every job hunting through 160,000 parts records. Solution: Kodexo Labs built an AI parts-lookup running inside their own self-hosted AWS VPC, keeping all data on their private cloud. Outcome: lookup time fell 85%, so a technician finds the right part in seconds, not minutes, on every daily job.
85%
Faster Lookup
160,000
Records Searched
Inc. 5000
Client
Extensiv
This $130M-funded logistics platform had operations staff waiting on engineers for every data question spread across 4 databases. Solution: Kodexo Labs built a LangGraph agentic system that reads plain-English questions and answers them directly. Outcome: staff now query 207 tables themselves, at over 90% accuracy, with answers arriving in seconds instead of days.
90%+
Accuracy
207
Tables
$130M+
Funded (Hg Capital)
SmartMedHx
Clinicians were losing visit time to manual note-taking during patient appointments. Solution: Kodexo Labs built HIPAA-compliant, patent-pending documentation AI that captures the conversation and writes the notes automatically, keeping patient data protected. Outcome: 42 providers now document visits hands-free, 493 patient interviews processed, and the client returned for a second, separate legal-tech build.
42
Providers
493
Interviews Processed
HIPAA
Compliant From Day One
What Clients Say About The Team
Fast-growing organisations do not applaud a consulting partner for polished slide presentations; they praise it for showing up when something actually breaks. The notes below come from founders who watched Kodexo Labs work the problem in real time.
Kodexo Labs has met all expectations; the team delivers on time and manages the project seamlessly. They respond promptly to needs and communicate effectively through virtual meetings, Google Chat, and WhatsApp. Overall, they're highly passionate about the project and excel in customer service.
Christopher Brigham
MD President, Brigham and Associates, Inc.
WATCH VIDEO
- HIPAA-aware model monitoringAudit-ready deployment pipelinesSub-100ms voice serving
Eight Industries Where Embedded MLOps Engineers Prevent Production Failures
Your models behave differently in each sector, and so do your compliance rules, traffic patterns, and raw data pipelines. The engineer you hire has already shipped production systems inside these eight verticals, not merely adjacent to them.
Ready to Put a Proven MLOps Engineer on Your Team?
You do not need a full department to stabilize your models. One embedded engineer, backed by a proven team that has shipped across dozens of industries, can close that reliability gap this quarter.
Your Auditors Ask Hard Questions; Your Engineer Answers Them
Regulated deployments fail when compliance is bolted on after launch. Your MLOps engineer works inside a 25-badge framework, so audits pass the first time. Seven badges govern model operations: SOC 2 Type II, HIPAA, GDPR, PCI-DSS, ISO/IEC 42001, NIST AI RMF, and ISO 27001.
EU AI Act
HIPAA
PCI-DSS
GDPR
CCPA
COPPA
SOC TYPE 2
ISO 27001
NIST AI RMF
FERPA
EU AI Act
HIPAA
PCI-DSS
GDPR
CCPA
COPPA
SOC TYPE 2
ISO 27001
NIST AI RMF
FERPA
Why Hiring Your MLOps Engineer Through Kodexo Labs Reduces Risk From Day One
Hiring an MLOps engineer is a bet on reliability under real traffic. These four proof points show what our embedded engineers have already delivered inside client teams, so you are not gambling on unproven claims.
Uptime You Can Prove
Listen AI is a healthcare voice platform. Our MLOps engineer built monitoring that holds 99.9% uptime and replies under 100 ms. The engineer you hire brings the same habits into your live stack.
Infrastructure You Fully Own
Diesel Laptops, an Inc. 5000 auto firm, needed its own models to run inside a private AWS VPC. Our engineer ran that setup and kept 160,000+ technical records in the client's own control.
You Hire Senior Engineers
A junior hire learning MLOps on your budget can stall your pipelines for months on end. Kodexo Labs, founded in 2021, staffs a PhD-level expert team, so onboarding takes days and not months.
Scope Before You Commit
Before a full engagement, we run a short discovery sprint to map your pipeline gaps and reliability targets. You watch the engineer work on your own stack, then decide if you want more.
Every Week Without Coverage Erodes Model Accuracy
An unmonitored model does not fail loudly. It drifts, quietly serving worse predictions until someone notices the drop in conversions. Every sprint you wait for the right hire, that gap keeps widening across your production systems.
Hire a senior MLOps engineer now and close the gap this sprint, with drift monitoring and alerting running live inside your systems.
Recognised By The Platforms That Vet AI Companies
Kodexo Labs is reviewed where technical buyers do their diligence: Clutch and Upwork. Every badge below links to the live profile.
Overcoming Hire MLOps Engineers Challenges
Hiring for MLOps fails in ways generic engineering hires do not. A candidate passes every coding round, then stalls on production pipelines your data scientists built. The wrong match costs weeks, silent model decay, and deployment schedules you cannot recover.
Every tool listed is in active production on a Kodexo Labs.
Every framework, runtime, and cloud service named here is running on a live client product right now. No theoretical stack, no resume keywords, no tools added for marketing weight.
How We Embed Your MLOps Engineer Fast
Our engineer-embedding process moves fast without cutting corners. Five phases take you from scoping the role to a fully embedded hire.
Discovery and Scoping
First, we map what you actually need. Your engineer's scope depends on your infrastructure stack, deployment targets, and compliance requirements. We define all three before shortlisting anyone, so the match fits your reality precisely.
Shortlist and Vetting
Next, we present senior and principal-level engineers matched to your stack. Each candidate is vetted for hands-on production experience, not interview polish. You review a short list of people who can do the work.
Pairing and Onboarding
Once you choose, we embed your engineer into your repository, CI/CD, and existing tooling within the first sprint. No slow ramp-up. They learn your conventions and start contributing to real pipelines while onboarding continues.
Live Production Integration
Now your engineer takes real ownership. They run deployment pipelines and monitoring inside your live systems, not a sandbox. Drift alerts, retraining, and incident response become their job, freeing your scientists to build models.
Delivery and Scaling
Delivery continues at a steady sprint cadence. As your workload grows, we scale the engagement up or add engineers. When the fit is right, contract-to-hire options let you bring your engineer onto your payroll permanently.
Related Insights
What is MLOps?
January 2024 · By Kodexo Labs
What MLOps is and how it works, covering MLOps vs DevOps, key components, tools, platforms, the MLOps engineer role, and leading MLOps companies.
IT Staffing Agencies – Kodexo Labs can Help Businesses Acquire Best Augmentation Solutions
July 2024 · By Kodexo Labs
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What is LLMOps? How to be Efficient in Business Optimization
January 2024 · By Kodexo Labs
What LLMOps is and how it optimizes large language model operations, covering open-source LLMs, architecture, tools, and generative AI vs LLMs.
Frequently Asked Questions
MLOps engineers deploy machine learning models into production and keep them running reliably. Using tools like AWS SageMaker, Kubeflow, and MLflow, they build the pipelines, monitoring, and automation that turn a data scientist's model into a dependable live service.