Senior DevOps ML Engineer

Location not identified

Pay: Pay not listed
Work setting: Remote — work setting source
This is a remote position. We are looking for a Senior DevOps ML Engineer to support a long-term enterprise AI platform focused on production-grade ML workloads. This role is fully centred on MLOps and DevOps infrastructure — ensuring that existing AI and ML models run reliably, securely, and at scale in production. The position operates in a regulated environment and requires strong focus on automation, governance, and operational excellence.
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Employment: Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting

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What you’ll work on

Full posting

Design, build and maintain MLOps and DevOps infrastructure on Azure
Develop and optimise ML pipelines for deployment, monitoring and governance
Implement CI/CD pipelines and automated ModelOps workflows
Apply Infrastructure as Code using Terraform
Monitor platform stability, performance, security and compliance

From the employer’s posting

Design, build, and maintain MLOps and DevOps infrastructure on Azure

Develop and optimise ML pipelines for deployment, monitoring, and governance

Implement CI/CD pipelines and automated ModelOps workflows

Apply Infrastructure as Code using Terraform

Monitor and ensure platform stability, performance, security, and compliance

What you’ll bring

All qualifications

Core experience

Proven background in DevOps or MLOps for AI / ML platforms
Infrastructure as Code expertise using Terraform
Solid experience with Azure Cloud services
Hands-on CI/CD and pipeline automation experience

Qualification wording

Proven background in DevOps or MLOps for AI / ML platforms

Infrastructure as Code expertise using Terraform

Solid experience with Azure Cloud services

Hands-on CI/CD and pipeline automation experience

Tools in this posting

Python
Azure
Databricks
Docker
Kubernetes
MLflow
Terraform

Source

Nice to have - Python development or scripting experience - Docker and Kubernetes knowledge

Responsibilities - Design, build, and maintain MLOps and DevOps infrastructure on Azure - Develop and optimise ML pipelines for deployment, monitoring, and governance

- Develop and optimise ML pipelines for deployment, monitoring, and governance - Work with Azure Databricks, MLflow, and Unity Catalog - Implement CI/CD pipelines and automated ModelOps workflows

Requirements - Strong hands-on experience with Azure Databricks, including MLflow and Unity Catalog - Proven background in DevOps or MLOps for AI / ML platforms

- Proven background in DevOps or MLOps for AI / ML platforms - Solid experience with Azure Cloud services - Hands-on CI/CD and pipeline automation experience

- Python development or scripting experience - Docker and Kubernetes knowledge - Exposure to Generative AI or broader ML workflows

- Ensure data architecture supports governance, lineage, and schema evolution - Apply Infrastructure as Code using Terraform - Collaborate closely with AI engineers and data teams to support production ML systems

- Hands-on CI/CD and pipeline automation experience - Infrastructure as Code expertise using Terraform - Strong understanding of data governance, access control, and compliance principles

Benefits in the posting

Full benefits wording

Comprehensive healthcare
Fully remote model

From the employer’s posting.

Posting record

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Open 4 monthsWe first saw this posting 149 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records: Live
First seen by us: Jun 3, 2026 · 149 days ago
Recorded sightings: 194
Last seen by us: Oct 29, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

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Job description

View original posting ↗

This is a remote position.

We are looking for a Senior DevOps ML Engineer to support a long-term enterprise AI platform focused on production-grade ML workloads. This role is fully centred on MLOps and DevOps infrastructure — ensuring that existing AI and ML models run reliably, securely, and at scale in production. The position operates in a regulated environment and requires strong focus on automation, governance, and operational excellence.

Responsibilities

Design, build, and maintain MLOps and DevOps infrastructure on Azure
Develop and optimise ML pipelines for deployment, monitoring, and governance
Work with Azure Databricks, MLflow, and Unity Catalog
Implement CI/CD pipelines and automated ModelOps workflows
Ensure data architecture supports governance, lineage, and schema evolution
Apply Infrastructure as Code using Terraform
Collaborate closely with AI engineers and data teams to support production ML systems
Monitor and ensure platform stability, performance, security, and compliance
Support operational readiness of ML workloads in regulated environments

Requirements

Strong hands-on experience with Azure Databricks, including MLflow and Unity Catalog
Proven background in DevOps or MLOps for AI / ML platforms
Solid experience with Azure Cloud services
Hands-on CI/CD and pipeline automation experience
Infrastructure as Code expertise using Terraform
Strong understanding of data governance, access control, and compliance principles
Confident English for daily cooperation with international stakeholders

Nice to have

Python development or scripting experience
Docker and Kubernetes knowledge
Exposure to Generative AI or broader ML workflows
Experience working in insurance or other regulated environments

Benefits

Solid, competitive salary
Work in multinational environment on international projects
Comprehensive healthcare
Long-term B2B contract with stable project pipeline
Fully remote model

Source details

View original posting ↗

Source notes

Source excerpts

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Pay: No pay amount identified in the saved description.
Location & working pattern: Location not supplied.
This is a remote position. We are looking for a Senior DevOps ML Engineer to support a long-term enterprise AI platform focused on production-grade ML workloads. This role is fully centred on MLOps and DevOps infrastructure — ensuring that existing AI and ML models run reliably, securely, and at scale in production. The position operates in a regulated environment and requires strong focus on automation, governance, and operational excellence.
More source context
- Long-term B2B contract with stable project pipeline - Fully remote model
Work authorization: No clear work-authorization passage found. Eligibility is unconfirmed.

Open source at madiffpl.zohorecruit.com ↗Read the full description ↓

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