---
title: "Model Deployment Engineer | MLOps Engineering Team | Azendo"
description: "Assign a Model Deployment Engineer to your roadmap under one monthly agreement, working from Thailand with a service delivery manager accountable for delivery."
url: "https://azendo.co/services/mlops-engineering/model-deployment-engineer/"
---

[← MLOps engineering](https://azendo.co/services/mlops-engineering/) 

# Model Deployment Engineer assigned to your roadmap.

A Model Deployment Engineer focuses on serving. Getting a trained model to respond fast enough, cheaply enough and reliably enough to sit in a product.

Assigned under one service agreement with a minimum monthly capacity in hours, run by a service delivery manager in Chiang Mai, Thailand — five hours ahead of Northern Europe.

[Contact us](https://azendo.co/get-in-touch/) [See pricing](https://azendo.co/pricing/) 

Part of our service line

[MLOps engineering](https://azendo.co/services/mlops-engineering/) 

This role is delivered as part of our MLOps engineering services. Take the role on its own, or the whole discipline as one delivery team.

## What a Model Deployment Engineer does on an MLOps team.

Skills and scope are agreed before delivery starts, so the first sprint is productive rather than a ramp-up month. The work runs to the same backlog, the same repositories and the same definition of done as your own team's.

### Model Deployment Engineer skills and technology we assign for

* [TorchServe](https://azendo.co/skills/torchserve/)
* [TensorFlow Serving](https://azendo.co/skills/tensorflow-serving/)
* [Triton](https://azendo.co/skills/triton/)
* [vLLM](https://azendo.co/skills/vllm/)
* [BentoML](https://azendo.co/skills/bentoml/)
* [ONNX Runtime](https://azendo.co/skills/onnx/)
* [Kubernetes](https://azendo.co/skills/kubernetes/)
* [GPU inference](https://azendo.co/skills/gpu-inference/)
* [quantisation](https://azendo.co/skills/quantisation/)
* [batching](https://azendo.co/skills/batching/)
* [A/B and shadow deployment](https://azendo.co/skills/a-b-and-shadow-deployment/)

* Building serving infrastructure with autoscaling and appropriate hardware.
* Inference optimisation through quantisation, batching and caching.
* Canary and shadow deployments so a new model version is validated on real traffic before it takes it.
* Load testing inference endpoints so autoscaling thresholds are set from evidence.
* Managing model warm-up and cold-start behaviour, which dominates perceived latency.

## Seniority levels we assign.

Senior. Serving optimisation is specialised and the gains are large when it matters. Seniority is one of the inputs in [how the monthly fee is built](https://azendo.co/pricing/).

## When a Model Deployment Engineer is the right assignment.

Specialist assignment where inference cost or latency has become the limiting factor. On an ongoing roadmap this role is most often assigned alongside an MLOps Engineer or a Machine Learning Engineer. All 4 roles in this service line can sit on the same agreement, and capacity moves between them at the monthly cycle rather than requiring a new contract. It is one of the assignments that make up [MLOps engineering](https://azendo.co/services/mlops-engineering/) at Azendo.

160 h

Typical monthly capacity for this role

Fixed

Monthly price, unaffected by leave or holidays

4–6 weeks

From signed scope to delivery starting

Monthly

Cycle to raise or lower committed hours

## Other roles in MLOps engineering.

[MLOps Engineer MLOps engineering role](https://azendo.co/services/mlops-engineering/mlops-engineer/)[Machine Learning Engineer MLOps engineering role](https://azendo.co/services/mlops-engineering/machine-learning-engineer/)[ML Platform Engineer MLOps engineering role](https://azendo.co/services/mlops-engineering/ml-platform-engineer/) 

The same delivery team, on your product, month after month.

Chiang Mai and Bangkok, Thailand — five hours ahead of Northern Europe

## Questions about assigning a Model Deployment Engineer.

How much can inference cost be reduced? 

It varies, but quantisation, batching and caching together often make a material difference. We measure before promising anything.

Can you deploy without downtime? 

Yes, through canary and shadow deployment, so a new model version is validated on real traffic before it takes any.

## Add model deployment engineer capacity to your roadmap.

Tell us the scope and the stack. We come back with the profile, the capacity and what the first month looks like, or you can [talk to a service delivery manager](https://azendo.co/get-in-touch/) first.

[Contact us](https://azendo.co/get-in-touch/) [How it works](https://azendo.co/how-it-works/) 

## Assign a Model Deployment Engineer to your roadmap.