- Muttdata
- Argentina,
- 16 days ago
Machine Learning / AI Engineer Senior - Databricks - Eng.
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Machine Learning / AI Engineer Senior - Databricks - Eng: our view in 3 lines...
- The Role:This is a senior machine learning and AI engineering role focused on production systems, Databricks and client delivery.
- The Person:The person will lead end-to-end AI and ML implementations with clients, build and maintain production pipelines, develop Generative and Agentic AI applications, architect AI and ML systems, and manage deployment infrastructure.
- Requirements:The role calls for 4+ years of experience with Generative or Agentic AI or Machine Learning systems, 3+ years in a customer-facing consulting or solutions architect role, Python, MLOps lifecycle management, Docker, data pipeline orchestration and Apache Spark.
About the role
Join Our Data Products and Machine Learning Development Remote Startup!
Mutt Data is a dynamic startup committed to crafting innovative systems using cutting-edge Big Data and Machine Learning technologies.
We’re looking for a Machine Learning / AI Engineer Senior to help take our expertise to the next level. If you consider yourself a data nerd like us, we’d love to connect!
Join Our Data Products and Machine Learning Development Remote Startup!
Mutt Data is a dynamic startup committed to crafting innovative systems using cutting-edge Big Data and Machine Learning technologies.
We’re looking for a Machine Learning / AI Engineer Senior to help take our expertise to the next level. If you consider yourself a data nerd like us, we’d love to connect!
What We Do
Our Partnerships
Our Values
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Responsibilities
directly with clients, translating complex business problems into robust technical solutions.
articulate technical findings, proposals, and project status to both technical and non-technical audiences.
continuous delivery (CI/CD), and advanced MLOps/AIOps practices to ensure reliability and scalability of
models.
experience with technologies like multi-agent orchestration, agent evaluation, and Retrieval Augmented
Generation(RAG) Systems in a production setting.
solutions based on customer requirements
in technologies such as Docker, pipeline orchestrators, and lakehouse database systems like Databricks.
performance data processing (e.g., using technologies like Apache Spark).
and knowledge transfer activities.
partner platforms.
Required Skills
or Machine Learning systems, with a mandatory requirement for productionizing and maintaining models in
a live environment.
implementation and delivery.
deployment best practices.
data pipeline orchestration.

