AI/ML Engineer – EV Charging Solutions

About the Role:

We are looking for a highly skilled AI/ML Engineer to help build a groundbreaking product that will replace traditional EV charging subject matter expertise with state-of-the-art AI systems. This role focuses on deploying intelligent agents into production and implementing supervision systems that ensure high accuracy (90%+). You will work with large language models (LLMs), retrieval-augmented generation (RAG), multi-agent architectures, and frameworks such as LangChain and LangGraph.

Key Responsibilities:

• Design, develop, and deploy AI-powered tools and agents that can autonomously handle complex EV charging tasks.
• Implement supervision systems to monitor and guarantee agent accuracy exceeding 90% across a variety of use cases.
• Build and maintain multi-agent systems integrated with real-time data and ensure their efficient, scalable deployment into production environments.
• Develop solutions using LLMs, RAG, and similar AI technologies to enhance decision-making and knowledge retrieval capabilities.
• Collaborate with cross-functional teams to ensure smooth deployment, scalability, and continuous improvement of AI models.
• Drive innovation by experimenting with and integrating the latest advancements in AI, ML, and autonomous agent technologies.

Qualifications:

• Proven experience in deploying AI agents into production environments, with a focus on achieving high-performance results.
• Expertise in building supervision systems that ensure a minimum of 90% answer accuracy across varied inputs.
• Proficiency with LangChain, LangGraph, or similar frameworks for developing and managing AI agents.
• Strong understanding of LLMs, RAG, and multi-agent architectures.
• Experience working with cloud platforms such as AWS, GCP, Azure, and Databricks.
• Familiarity with containerization tools (Docker, Kubernetes).
• Strong analytical and problem-solving skills in a fast-paced, innovative environment.

How to Apply:

Please submit your application along with a brief response to the following question:

Can you describe a project where you built and deployed an LLM-based agent? What tools did you use, and what was the scale?