Salary
≈ $40k – $106k per year (Estimated)
Location
Remote (Colombia, Uruguay)
Seniority
Senior
Employment
Full-Time
Confirmed on the employer's own hiring board on Oct 31, 2026. First seen by Alion on Oct 21, 2026. DataArt scores B on the Alion truth index.
Overview
Company
Impact
Profile match
DataArt is a global software engineering and consulting firm headquartered in New York that builds and modernises custom software, data platforms and cloud infrastructure for clients in finance, travel, healthcare, media and retail. Founded in 1997, it runs delivery centres across Europe, the Americas and Asia, including offices in Wroclaw, Lublin and Lodz in Poland and teams in India, and works with more than 400 enterprise clients. Its openings include senior .NET, Java, Python and frontend engineers, DevOps and site reliability engineers, data architects and data engineers, QA automation leads and solution consultants.
We are looking for a Middle ML AI Developer with strong Python, cloud, and infrastructure experience to design, build, and operationalize GenAI based solutions. In this role, you will contribute to AI products, support platform adoption, and help improve delivery through engineering best practices.
AI Platform is focused on expanding a modern AI portfolio through GenAI based solutions and shared platform capabilities. You will contribute to building scalable AI products, supporting domain teams, and improving the adoption of platform standards across squads.
- Develop data products and AI and GenAI solutions within domain teams
- Facilitate adoption of the Data and AI Platform across squads
- Build, deploy, and operate GenAI and ML models in production ready environments
- Manage infrastructure for environments and AI and ML products with focus on observability, performance, and reliability
- Contribute to CI/CD pipelines, infrastructure as code practices, and platform automation
- Collaborate with Software Engineering, Data, MLOps, and DevOps teams to maintain engineering standards
- Support experimentation frameworks and internal tools for GenAI model development and evaluation
- Experience with GCP or a similar cloud provider such as AWS or Azure
- Experience with Terraform or other infrastructure as code tools
- Strong knowledge of Python
- Backend engineering experience with APIs and services
- Experience with GenAI or ML and MLOps practices including Airflow, MLflow, pipelines, or monitoring
- Solid understanding of CI/CD, Docker, and software engineering best practices
- Familiarity with model deployment, model serving, and operating ML and AI systems in production
- Experience with observability, incident response, or platform operations
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