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.
Why Empiryx
Why hire MLOps engineers from Empiryx?
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
Share Your ML Stack
Tell us what models you're running and where they're breaking down.
Get Matched in 48 Hours
We surface pre-vetted MLOps engineers from our curated network.
Interview & Select
You interview and choose who joins your team — full control.
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.
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RAG, fine-tuning, and AI agents built for production, not demos.
DevOps
CI/CD, infrastructure as code, and observability that scales.
Application Scaling
Performance audits and fixes that take your MVP to production-ready.
Database & Data Engineering
Database design, pipelines, and migrations built to last.