- Muttdata
- Argentina,
- 16 days ago
Machine Learning / AI Engineer Lead - Databricks - Eng.
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Machine Learning / AI Engineer Lead - Databricks - Eng: our view in 3 lines...
- The Role:This role is for a lead machine learning and AI engineer focused on client-facing production AI and ML systems, including generative and agentic AI.
- The Person:The person will lead end-to-end AI and ML project implementations, design and maintain production AI and ML pipelines, build generative and agentic AI applications, and document client engagements and training.
- Requirements:The role requires 4+ years of experience, 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 Lead 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 Lead 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
Curious about what we’re up to? Check out our case studies and dive into our blog post to learn more about our culture and the exciting projects we’re working on!
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.

