AI Engineer

Role: AI Engineer

Location: Minneapolis, MN(Remote)

Duration: 12 Months

Job Description:

Proficient in Python and other relevant programming languages.
 
Qualifications:

• 3 years of hands-on experience with machine learning frameworks and libraries (such as TensorFlow, PyTorch, or similar).
• 2 years of hands-on experience in developing and deploying AI agents and machine learning models.   
• ·Machine learning frameworks and libraries (such as TensorFlow, PyTorch, or similar), developing and deploying AI agents and machine learning models, Demonstrate deep expertise in cloud technologies, with a strong focus on Microsoft Azure, including expertise in Azure Data and AI platforms (such as Databricks, Fabric, and AI Foundry), Build and experiment with GenAI models and agentic workflows
• ·Design and implement intelligent AI agents leveraging large language models (LLMs), planning algorithms, and decision-making frameworks.
• Build secure, scalable AI agents and integrate into applications and workflows for robust, cross-platform deployment and enhanced user experience.
• Advance AI agent capabilities through research and performance evaluation, focusing on improved conversation, decision-making, adaptability, and the implementation of safety and guardrail mechanisms.
• Continuously optimize agent performance through feedback mechanisms, reinforcement learning, and user interaction analysis.
• Demonstrate deep expertise in cloud technologies, with a strong focus on Microsoft Azure, including expertise in Azure Data and AI platforms (such as Databricks, Fabric, and Microsoft AI Foundry).
• Build and experiment with GenAI models and agentic workflows, contributing to the development of intelligent security solutions.
• Collaborate across the stack, supporting backend services, model pipelines, and frontend interfaces for agentic systems.
• Prototype rapidly, iterate on ideas, and contribute to incubation efforts in a startup-style environment.
• Work closely with senior engineers and researchers, learning best practices and contributing to production-grade AI systems