Senior Machine Learning / AI Engineer - Databricks
New
J
JobgetherArtificial Intelligence
Based in Brazil; the original listing identifies the role as remote within Latin AmericaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- At least 4 years of hands-on professional experience developing, deploying, and managing Generative AI, Agentic AI, or Machine Learning systems; at least 3 years of experience in client-facing consulting or solutions architecture.
- Required Skills
- DockerPythonMachine LearningSparkCI/CDDatabricksMLOpsGenerative AI
Requirements
- At least 4 years of hands-on professional experience developing, deploying, and managing Generative AI, Agentic AI, or Machine Learning systems.
- Experience productionizing and maintaining models in live environments.
- At least 3 years of experience in client-facing consulting or solutions architecture focused on technical implementation and delivery.
- Strong Python programming skills for data-intensive, scalable AI/ML applications.
- Expertise in MLOps lifecycle management, including model versioning, testing, monitoring, continuous integration, and automated deployment.
- Practical experience managing AI/ML infrastructure, including Docker containerization and data pipeline orchestration.
- Familiarity with distributed computing frameworks such as Apache Spark and lakehouse technologies such as Databricks.
- Hands-on experience developing production-ready Generative AI and Agentic AI solutions, including multi-agent systems, agent evaluation, and RAG architectures.
- Ability to communicate and collaborate with clients, stakeholders, and multidisciplinary teams.
Responsibilities
- Lead end-to-end AI and Machine Learning implementations, translating client business needs into technical solutions.
- Communicate technical findings, proposals, implementation plans, and project progress to technical and non-technical stakeholders.
- Design, develop, deploy, and maintain production-grade AI/ML pipelines using CI/CD and MLOps/AIOps practices.
- Build Generative AI and Agentic AI applications, including multi-agent orchestration, agent evaluation, and RAG systems.
- Architect solutions combining traditional Machine Learning and Generative AI to meet customer requirements.
- Manage production deployment infrastructure using Docker, pipeline orchestration tools, and Databricks.
- Apply Apache Spark and distributed computing technologies to scalable Machine Learning workloads and data processing.
- Contribute technical work, collaboration, and knowledge transfer to the AI Forward Deployed Engineering practice.
- Document client engagements, project deliverables, and training activities through partner platforms.
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