Lead 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 deploying Generative and Agentic AI applications, with a strong focus on MLOps lifecycle management and infrastructure management using Docker and data pipeline orchestration. Proven ability to translate complex business problems into technical solutions while effectively communicating with both technical and non-technical stakeholders.

Highest-signal resume keywords

Generative AI ImplementationMLOps Lifecycle ManagementPython ProgrammingDocker ContainerizationClient-Facing Consulting

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningAI Pipeline DevelopmentModel ProductionizationData ProcessingModel VersioningAutomated DeploymentMulti-Agent OrchestrationRetrieval Augmented GenerationInfrastructure ManagementData Pipeline Orchestration

Soft Skills

Excellent Communication SkillsContinuous Learning

Tools & Technologies

Apache SparkDatabricksCI/CD PracticesAIOps

Industry Keywords

AI ConsultingTechnical SolutionsClient EngagementKnowledge Transfer

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
Articulate technical findings, proposals, and project status to technical and non-technical audiences
Design, build, and maintain production-grade AI and 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 Retrieval Augmented Generation (RAG) systems
Architect end-to-end traditional Machine Learning and Generative AI solutions based on customer requirements
Manage infrastructure for production deployment using Docker, pipeline orchestrators, and lakehouse database systems such as Databricks
Leverage distributed computing frameworks such as Apache Spark for scalable machine learning and high-performance data processing
Contribute to the 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
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
Demonstrable experience with infrastructure management, including 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

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