Machine Learning / AI Engineer Senior - Databricks - Eng
Argentina
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What youโll work on
Full postingLead end-to-end AI/ML project implementations directly with clients
Design, build and maintain production AI and ML pipelines with CI/CD and MLOps
Build generative and agentic AI applications, including RAG systems
Architect traditional ML and generative AI solutions to customer requirements
Manage infrastructure for production deployment using Docker, orchestrators and Databricks
From the employerโs posting
leading and executing end-to-end AI/ML project implementations directly with clients
Design, build, and maintain production-grade AI and ML pipelines, focusing on continuous integration, continuous delivery (CI/CD), and advanced MLOps/AIOps practices
Implement and optimize cutting-edge Generative and Agentic AI applications
Architect end-to-end AI/ML systems, designing both traditional Machine Learning- and Generative AI-based solutions based on customer requirements
Manage underlying solution infrastructure necessary for production deployment
Tools in this posting
- Python
- AWS
- Azure
- Databricks
- dbt
- Docker
- Spark
- GCP
Source โ Tool mentions in context
- Demonstrable experience with infrastructure management, encompassing containerization (Docker) and data pipeline orchestration. - Expertise in Python for data-intensive and scalable AI/ML applications. - Demonstrated commitment to continuous learning in emerging AI/ML fields.
๐ Our Partnerships - Amazon Web Services - Astronomer
- Remote-first culture โ work from anywhere! ๐ - AWS, DBT, Google Cloud, Azure & Databricks certifications fully covered - In-Company English Lessons.
- Astronomer - Databricks ๐ Our Values
- Architect end-to-end AI/ML systems, designing both traditional Machine Learning- and Generative AI-based solutions based on customer requirements - Manage underlying solution infrastructure necessary for production deployment, demonstrating proficiency in technologies such as Docker, pipeline orchestrators, and lakehouse database systems like Databricks. - Leverage expertise in distributed computing frameworks, specifically in scalable machine learning and high- performance data processing (e.g., using technologies like Apache Spark).
- Expertise in MLOps lifecycle management, including model versioning, testing, monitoring, and automated deployment best practices. - Demonstrable experience with infrastructure management, encompassing containerization (Docker) and data pipeline orchestration. - Expertise in Python for data-intensive and scalable AI/ML applications.
- Manage underlying solution infrastructure necessary for production deployment, demonstrating proficiency in technologies such as Docker, pipeline orchestrators, and lakehouse database systems like Databricks. - Leverage expertise in distributed computing frameworks, specifically in scalable machine learning and high- performance data processing (e.g., using technologies like Apache Spark). - Contribute to the strategic growth of the AI FDE Practice, including participation in technical assignments and knowledge transfer activities.
Posting record
Report an errorActively hiringFirst seen by us 12 days ago, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Oct 15, 2026 ยท 12 days ago
- Recorded sightings
- 15
- Last seen by us
- Oct 28, 2026
These dates show when we found the posting. Check the employerโs website to confirm it is still accepting applications.
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Job description
๐ What We Do
- Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.
- Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.
- Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.
- Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.
- Building deep learning models, we enhance visual recognition and automation for various industries, improving product categorization, quality control, and information retrieval.
- Developing recommendation models, we personalize user experiences in e-commerce, streaming, and digital platforms, driving engagement and conversions.
๐ Our Partnerships
- Amazon Web Services
- Astronomer
- Databricks
๐ Our Values
- ๐ We are Data Nerds
- ๐ค We are Open Team Players
- ๐ We Take Ownership
- ๐ We Have a Positive Mindset
Responsibilities ๐ค
- Serve as a primary technical consultant, leading and executing end-to-end AI/ML project implementations
directly with clients, translating complex business problems into robust technical solutions. - Exhibit excellent communication, presentation, and stakeholder management skills to clearly
articulate technical findings, proposals, and project status to both technical and non-technical audiences. - ย Design, build, and maintain production-grade AI and ML pipelines, focusing on continuous integration,
continuous delivery (CI/CD), and advanced MLOps/AIOps practices to ensure reliability and scalability of
models. - Implement and optimize cutting-edge Generative and Agentic AI applications, demonstrating hands-on
experience with technologies like multi-agent orchestration, agent evaluation, and Retrieval Augmented
Generation(RAG) Systems in a production setting. - Architect end-to-end AI/ML systems, designing both traditional Machine Learning- and Generative AI-based
solutions based on customer requirements - Manage underlying solution infrastructure necessary for production deployment, demonstrating proficiency
in technologies such as Docker, pipeline orchestrators, and lakehouse database systems like Databricks. - Leverage expertise in distributed computing frameworks, specifically in scalable machine learning and high-
performance data processing (e.g., using technologies like Apache Spark). - Contribute to the strategic growth of the AI FDE Practice, including participation in technical assignments
and knowledge transfer activities. - Ensure all client engagements and training activities are properly documented and reported via designated
partner platforms.
Required Skills ๐ป
- 4+ years of hands-on professional experience developing, deploying, and managing Generative/Agentic AI
or Machine Learning systems, with a mandatory requirement for productionizing and maintaining models in
a live environment. - 3+ years of experience in a customer-facing consulting or solutions architect role, focused on technical
implementation and delivery. - Excellent verbal and written communication skills for effective client and internal team interaction.
- Expertise in MLOps lifecycle management, including model versioning, testing, monitoring, and automated
deployment best practices. - Demonstrable experience with infrastructure management, encompassing containerization (Docker) and
data pipeline orchestration. - Expertise in Python for data-intensive and scalable AI/ML applications.
- Demonstrated commitment to continuous learning in emerging AI/ML fields.
๐ Perks
- Remote-first culture โ work from anywhere! ๐
- AWS, DBT, Google Cloud, Azure & Databricks certifications fully covered
- In-Company English Lessons.
- Birthday off + an extra vacation week (Mutt Week! ๐๏ธ)
- Referral bonuses โ help us grow the team & get rewarded!
- Maslow: Monthly credits to spend in our benefits marketplace.
- โ๏ธ๐๏ธ Annual Mutters' Trip โ an unforgettable getaway with the team!
- ๐ถ Monthly Childcare Reimbursementย โ Because supporting families matters too
Employment type
Remote - Latam
Source details
Source notes
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- Pay
No pay amount identified in the saved description.
- Location & working pattern
Argentina
๐ Join Our Data Products and Machine Learning Development Remote Startup! ๐ Mutt Data is a dynamic startup committed to crafting innovative systems using cutting-edge Big Data and Machine Learning technologies.
More source context
๐ Perks - Remote-first culture โ work from anywhere! ๐ - AWS, DBT, Google Cloud, Azure & Databricks certifications fully covered
More relevant text appears in the full description.
- Work authorization
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