MLOps Developer with Python
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Location
Remote (Colombia, Uruguay)
Seniority
Middle · 3+ years exp
Employment
Full-Time
Confirmed on the employer's own hiring board on Nov 3, 2026. First seen by Alion on Oct 26, 2026. DataArt scores B on the Alion truth index.
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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 hands on MLOps and AI Ops engineer to help build, deploy, and operate ML and GenAI products on a shared Data and AI platform. You will focus on execution across model lifecycle activities, production reliability, and the infrastructure that supports both platform environments and delivered products.
Airline Platform is a cross domain Data and AI platform that enables teams to deliver reliable, secure, and scalable data and AI and ML products. It uses Google Cloud Platform, BigQuery, Docker, Kubernetes, Terraform, CI and CD pipelines, and GenAI tooling to support the full product lifecycle.
- Build, deploy, and operate ML and GenAI models and services end to end following MLOps practices
- Develop and maintain data and AI products within the platform domains
- Enable and harden the shared Data and AI platform including environment and product infrastructure
- Orchestrate and maintain reliable data and model pipelines ensuring quality and availability for downstream consumers
- Apply Infrastructure as Code with Terraform and CI and CD standards for data and AI workloads
- Instrument observability and monitoring for models and services and act on performance and cost
- Participate in design and code reviews
- Document the processes built
- Around 3 years of proven experience as an MLE, ML Ops, AI engineer, or similar role
- Strong Python with experience deploying and serving models in production
- Hands on end to end MLOps including model registry, deployment, and monitoring on generic platforms
- Experience with containerization and orchestration using Docker and Kubernetes
- Experience with Infrastructure as Code using Terraform and CI and CD practices
- Experience with Google Cloud Platform including BigQuery
- Fluency in Spanish and intermediate English for documentation and international collaboration
- Experience with GenAI engineering including LangChain, agents, Model Context Protocol, and vector databases
- Experience with API development using FastAPI or Flask
- Knowledge of data platform fundamentals including lakehouse and warehouse concepts, streaming, SQL transformations, and pipeline orchestration
- Awareness of data governance, cataloging, security, and IAM
- Experience with observability tooling such as Prometheus, Grafana, or Datadog
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