AI Engineer

Zartis

Remote

Full Time

Posted May 12, 2026

Not Specified

Remote

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Job Description

An AI Engineer responsible for developing and deploying advanced AI systems, including multi-agent workflows, LLM-powered applications, and RAG pipelines, primarily within cloud ecosystems like AWS and Databricks, to support innovative projects in the green energy industry.

Key Responsibilities

Build and extend multi-agent systems and agentic workflows using orchestration frameworks.
Develop and integrate Retrieval-Augmented Generation (RAG) pipelines for internal tools.
Implement LLM-powered chatbots, assistants, and autonomous agents for specific business use cases.
Collaborate with team leads to translate requirements into scalable, reliable AI solutions.
Harden proof-of-concept AI systems to production standards.
Pipeline AI components within AWS and Databricks ecosystems to ensure end-to-end workflows.
Apply best practices in observability, logging, and monitoring for deployed AI systems.
Contribute to CI/CD processes for model and prompt deployment.
Mentor and support other AI engineers.
Communicate technical progress, blockers, and decisions to stakeholders.
Participate in architectural discussions and decisions for AI systems.

Requirements

Solid background in data science, data engineering, or a related discipline, with practical AI ML experience.
Proven, hands-on experience building agentic AI systems, LLM-powered applications, and Retrieval-Augmented Generation (RAG) pipelines, not just training classifiers or regressors.
Strong working knowledge of the AWS ecosystem, particularly services relevant to AI ML workloads such as AWS Agent Core, AWS Bedrock, SageMaker, or AWS Runs.
Experience with Databricks for data engineering, ML pipelines, or model serving.
Ability to work independently, manage your own delivery, and produce clean, maintainable code.