Machine Learning / AI Engineer Lead

M
Mutt DataAI/ML
Remote - LatamFull-TimeLead
Salary not disclosed
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Job Details

Experience
4+ years of hands-on professional experience developing, deploying, and managing Generative/Agentic AI or Machine Learning systems; 3+ years of experience in a customer-facing consulting or solutions architect role
Required Skills
DockerPythonMachine LearningSparkDatabricksMLOpsGenerative AI

Requirements

  • Have 4+ years of hands-on professional experience developing, deploying, and managing Generative/Agentic AI or Machine Learning systems.
  • Have experience productionizing and maintaining models in a live environment.
  • Have 3+ years of experience in a customer-facing consulting or solutions architect role focused on technical implementation and delivery.
  • Demonstrate expertise in MLOps lifecycle management, including model versioning, testing, monitoring, and automated deployment.
  • Have experience managing infrastructure, including containerization with Docker and data pipeline orchestration.
  • Demonstrate expertise in Python for data-intensive and scalable AI/ML applications.
  • Show commitment to continuous learning in emerging AI/ML fields.

Responsibilities

  • Lead and execute end-to-end AI/ML project implementations directly with clients, translating business problems into technical solutions.
  • Design, build, and maintain production-grade AI/ML pipelines using CI/CD and MLOps/AIOps practices.
  • Implement and optimize Generative and Agentic AI applications, including multi-agent orchestration, agent evaluation, and RAG systems.
  • Architect traditional Machine Learning and Generative AI systems based on customer requirements.
  • Manage production solution infrastructure, including Docker, pipeline orchestrators, and Databricks lakehouse systems.
  • Use distributed computing frameworks such as Apache Spark for scalable machine learning and high-performance data processing.
  • Contribute to the AI FDE Practice through technical assignments and knowledge transfer.
  • Document and report client engagements and training activities through designated partner platforms.
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