Dedicated model deployment engineers for your offshore team.
Offshore model deployment engineers in Chiang Mai and Bangkok, working full time on your roadmap and managed by us. A model deployment engineer focuses on serving: getting a trained model to respond fast, cheaply and reliably inside your product.
One agreement and one fixed monthly fee, with a service delivery manager in Thailand who runs your team day to day.
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 discipline 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 answers for the work, training and coaching, the office and equipment, and HR and payroll. The price below is for a mid-level Model Deployment Engineer; junior and senior levels are priced in your proposal, and how the monthly fee is built explains the arithmetic.
From price
One mid-level Model Deployment Engineer, full time, everything below included. All 27 things included ››
- Model deployment engineer From USD 4,200 per month
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Service
We manage and develop the team
- A service delivery manager who runs the team and answers for 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 specialist
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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 own 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 an 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.
Senior. Serving optimisation is specialist work, and the gains are large when it matters. Seniority is one of the inputs into 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, alongside an MLOps or machine learning engineer. Model deployment engineers join as part of MLOps engineering at Azendo.
What we look for in a dedicated Model Deployment Engineer.
We look for serving specialists 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.
Your remote Model Deployment Engineer's first three months.
- 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, and quantisation, batching and caching together often make a material difference. Our engineer measures before recommending changes.
Can you deploy without downtime?
Yes, with canary and shadow deployments, so a new model version is validated on real traffic first.
Add remote model deployment engineers to your team.
Tell us your roadmap and your stack, and we'll come back with the right profile, the capacity and a start date. You can also talk to a service delivery manager first.
Add model deployment engineers to your remote team.
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