MLOps

MLOps engineers who keep your models running in production

Model serving, monitoring, versioning, and CI/CD for machine learning — built by engineers who've kept ML systems alive at scale.

150+
Models in production
7–10
Days to hire
70%
Cost savings
99.9%
Uptime maintained

Why Empiryx

Why hire MLOps engineers from Empiryx?

Engineers who've run ML systems at real production scale
Model versioning and rollback built in from day one
70% cost savings vs US/UK MLOps hires
7–10 day average time to hire
Drift detection so model quality never silently degrades
Works with your existing cloud provider — AWS, GCP, or Azure

Expertise

MLOps skills we specialize in

01

Model Serving & Deployment

Low-latency inference endpoints, batching, and autoscaling for models in production.

02

Monitoring & Drift Detection

Tracking prediction quality, data drift, and performance decay before it hits your users.

03

ML CI/CD Pipelines

Automated training, testing, and deployment pipelines so model updates ship safely.

04

Vector Databases & Feature Stores

Infrastructure for embeddings and features that both training and serving can rely on.

Expertise

Roles & capabilities we specialise in

A deeper look at the specialists we place and the work they ship.

Model Serving & Inference

Low-latency endpoints with batching and autoscaling.

ML CI/CD

Automated training, testing, and deployment pipelines.

Drift & Quality Monitoring

Catch prediction decay before users do.

Feature Stores

Shared, versioned features for training and serving.

Vector DB Operations

Pinecone, Weaviate, and pgvector at production scale.

Model Registry & Versioning

Reproducible models with safe rollback.

Experiment Tracking

MLflow, W&B, and internal lineage.

Pipeline Orchestration

Airflow, Kubeflow, and Dagster for training runs.

GPU & Inference Cost Optimisation

Right-sized compute for your load.

ML Observability

Metrics, traces, and logs across the model lifecycle.

A/B & Shadow Deployments

Safe rollout patterns for new models.

ML Platform Engineering

Internal platforms your teams actually want to use.

Industries we serve

Built for high-stakes sectors

Domain-aware engineers who understand your sector's constraints — not just the syntax.

Fintech & Banking

Credit and risk model operations

Healthcare & Medtech

Diagnostic model lifecycle

Retail & E-commerce

Recommendation serving at scale

Logistics & Supply Chain

Forecast pipelines and routing

Manufacturing & Industry 4.0

Predictive maintenance models

Automotive & Mobility

Perception model deployment

EdTech & Education

Personalisation model ops

Real Estate & PropTech

Valuation model monitoring

FAQ

Answers to common questions

Process

How it works

01

Share Your ML Stack

Tell us what models you're running and where they're breaking down.

02

Get Matched in 48 Hours

We surface pre-vetted MLOps engineers from our curated network.

03

Interview & Select

You interview and choose who joins your team — full control.

04

Start in 7–10 Days

Engineers onboard and start hardening your ML infrastructure.

Get started

Ready to make your ML systems reliable?

Stop babysitting models manually — let engineers automate it.

Let's talk

Tell us what you're building

Share your engineering goals and the Empiryx team will respond within 24 hours with a tailored path forward.