Dedicated model deployment engineers for your offshore team.
Offshore model deployment engineers in Chiang Mai and Bangkok, working full time on your product and managed by us. A model deployment engineer focuses on serving: getting a trained model to respond fast, cheaply and reliably inside your product.
You direct the work. We manage the people day to day, for one fixed monthly fee.
This role is part of a dedicated MLOps engineering team. It's staff augmentation, fully managed: take one role on its own, or the whole area as one delivery team.
One Model Deployment Engineer, assigned to your product and fully managed.
We assign a Model Deployment Engineer to your team full time and run everything around them: a service delivery manager who oversees delivery, training and coaching, the office and equipment, and HR and payroll. The price below is for a junior Model Deployment Engineer; other levels are priced in your proposal, and how the monthly fee is built explains the arithmetic.
From price
One junior Model Deployment Engineer, full time, everything below included. All 27 things included ››
- Model deployment engineer From USD 3,000 per month
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Service
We manage and develop the team
- A service delivery manager who oversees operations and delivery
- A 1:1 with our Head of Delivery every two weeks
- Weekly delivery scoring and monthly capacity reports
- A Talent Success Manager for every team member
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HR
We are the employer
- Recruitment and technical assessment
- Employment contracts
- Salary and payroll
- Tax and social security
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Facilitation
We provide the workplace
- A desk in our office in Chiang Mai or Bangkok
- A Workplace Experience Manager on site
- Team events through the year
- IT support
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Equipment
We supply the tools
- Laptop and hardware
- Software licences
- LinkedIn Learning access
- Device security and management
What a Model Deployment Engineer does on a dedicated MLOps team.
They work with your ML and infrastructure engineers on latency and cost targets, and roll out new model versions without disrupting your users.
Model Deployment Engineer skills and technologies
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Building serving infrastructure with autoscaling and appropriate hardware.
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Inference optimisation through quantisation, batching and caching.
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Canary and shadow deployments so a new model version is validated on real traffic before it takes it.
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Load testing inference endpoints so autoscaling thresholds are set from evidence.
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Managing model warm-up and cold-start behaviour, which dominates perceived latency.
Model Deployment Engineer seniority levels.
Junior to lead. Serving optimisation is specialist work, and the gains are largest when a senior engineer leads it. Weigh those gains against the level when you look at how the monthly fee is built.
When your team needs a Model Deployment Engineer.
The right addition when inference cost or latency is holding your product back. Serving is one of the specialisms in MLOps engineering at Azendo, next to the MLOps and machine learning engineers. It helps companies that hire a dedicated development team and need inference fast, cheap and reliable.
What we look for in a dedicated Model Deployment Engineer.
We look for serving engineers who can quantise, batch and cache without hurting quality. They should load test endpoints before choosing autoscaling settings, and roll out new versions safely with canary or shadow traffic.
A remote Model Deployment Engineer's first three months on your team.
- Weeks 1 to 2Profiling your current serving setup for latency and cost.
- Weeks 3 to 6Optimising inference and load testing the endpoints.
- Months 2 to 3Owning serving, including canary releases and cold-start behaviour.
Questions about adding a Model Deployment Engineer to your team.
How much can inference cost come down?
It varies with the model and the traffic, and quantisation, batching and caching together often make a material difference. Our engineer measures latency, throughput and cost before recommending changes. Each optimisation is then tested against an evaluation set, so savings never come at the expense of the quality your users see.
Can you deploy without downtime?
Yes, with canary and shadow deployments, so a new model version is validated on real traffic before it serves everyone. If quality or latency drops, traffic rolls back automatically to the previous version. Every release is versioned and logged, so you can see which model answered any request.
Add remote model deployment engineers to your team.
Tell us which models you serve, the latency you need and what inference costs you today. We'll suggest the level that fits and send the fixed monthly fee, and you can talk to a service delivery manager before anything is signed.
Tell us what your model deployment engineer would work on.
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