Senior Machine Learning, AI Engineer

MUTT DATA

. Serve as a primary technical consultant, leading and executing end-to-end AI/ML project implementations directly with clients .

Posted 10/14/2026full-timeRemote • ArgentinaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and managing Generative and Agentic AI applications, with a strong focus on MLOps lifecycle management and production-grade AI/ML pipeline implementation. Proven ability to translate complex business challenges into effective technical solutions while effectively communicating with diverse audiences.

Highest-signal resume keywords

Generative AI DevelopmentMLOps Lifecycle ManagementPython ProgrammingDocker ContainerizationClient-Facing Consulting

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningAI Application DevelopmentModel VersioningAutomated DeploymentData Pipeline OrchestrationMulti-Agent OrchestrationAgent EvaluationRAG SystemsProduction DeploymentScalable Data Processing

Soft Skills

Excellent Communication SkillsProblem-SolvingContinuous Learning

Tools & Technologies

DockerApache SparkDatabricksCI/CD PracticesPipeline Orchestrators

Industry Keywords

AI/ML Project ImplementationTechnical ConsultingClient EngagementKnowledge TransferProductionizing Models

Tech Stack

Tools & technologies

ApacheDockerPythonSpark

About the role

Key responsibilities & impact

Serve as a primary technical consultant, leading and executing end-to-end AI/ML project implementations directly with clients
Translate complex business problems into robust technical solutions
Present technical findings, proposals, and project status to technical and non-technical audiences
Design, build, and maintain production-grade AI/ML pipelines with CI/CD and advanced MLOps/AIOps practices
Implement and optimize Generative and Agentic AI applications, including multi-agent orchestration, agent evaluation, and RAG systems
Architect end-to-end traditional Machine Learning and Generative AI solutions based on customer requirements
Manage production deployment infrastructure using Docker, pipeline orchestrators, and lakehouse databases such as Databricks
Leverage distributed computing frameworks such as Apache Spark for scalable machine learning and high-performance data processing
Contribute to strategic growth of the AI FDE Practice through technical assignments and knowledge transfer
Document and report client engagements and training activities via designated partner platforms

Requirements

What you’ll need

4+ years of hands-on professional experience developing, deploying, and managing Generative/Agentic AI or Machine Learning systems, with mandatory experience 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
Expertise in MLOps lifecycle management, including model versioning, testing, monitoring, and automated deployment best practices
Experience with infrastructure management, including containerization with 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

Benefits

Comp & perks

Remote-first culture – work from anywhere
AWS, DBT, Google Cloud, Azure & Databricks certifications fully covered
In-Company English Lessons
Birthday off + an extra vacation week (Mutt Week!)
Referral bonuses
Maslow: Monthly credits to spend in our benefits marketplace
Annual Mutters' Trip
Monthly Childcare Reimbursement