Job Summary:
We are seeking a highly skilled and creative Entry Level - Generative AI Engineer to apply state-of-the-art generative models to solve complex challenges in automotive engineering. This role focuses on creating intelligent agents that leverage generative capabilities for reasoning, planning, and executing complex tasks autonomously. The ideal candidate will bridge the gap between generative AIs creative potential and agentic AIs autonomous action, developing systems that can understand, reason, and act in dynamic environments.
Key Responsibilities
Integrated AI System Development:
Design and build AI agents that utilize large language models for reasoning and decision-making
Develop systems where generative AI components enable sophisticated planning and problem-solving
Create autonomous agents capable of using tools, APIs, and external systems through generative interfaces
Implement multi-agent systems where generative AI facilitates communication and collaboration
Generative AI Capabilities:
Fine-tune and optimize large language models for specific agentic tasks
Develop prompt engineering strategies for complex reasoning and chain-of-thought processes
Implement RAG (Retrieval-Augmented Generation) systems to enhance agent knowledge and context
Create generative models for code generation, content creation, and strategic planning within agent frameworks
Agent Architecture & Autonomy:
Build reflective agents that can critique and improve their own reasoning processes
Design goal-oriented systems that use generative AI for planning and adaptation
Implement memory architectures that allow agents to learn from experience and maintain context
Develop safety mechanisms and oversight for autonomous generative agents
Multi-Modal Agent Systems:
Integrate vision, language, and action capabilities within agent frameworks
Develop agents that can process and generate across multiple modalities (text, image, audio)
Create embodied agents that interact with digital and physical environments
Research & Innovation: Stay current with the latest academic research and open-source advancements in generative AI. Prototype new ideas and conduct experiments to validate their feasibility and impact.
Education: Ph.D in Computer Science, Electrical Engineering, Mechanical Engineering or related streams.
Technical Proficiency:
Experience with generative AI (LLMs, diffusion models, generative architectures)
Experience with agentic AI systems, reinforcement learning, or autonomous systems
Strong programming skills in Python and experience with AI/ML frameworks (PyTorch, TensorFlow)
Experience with LangChain, AutoGPT, Microsoft Autogen, or similar agent frameworks
Proficiency with transformer architectures and fine-tuning techniques
Deep understanding of prompt engineering, reasoning techniques, and LLM capabilities
Experience with RAG systems, vector databases, and knowledge retrieval
Knowledge of reinforcement learning, planning algorithms, and decision-making systems
Familiarity with multi-agent systems and emergent behavior
Ph.D
Job Summary:
We are seeking a highly skilled and creative Entry Level - Generative AI Engineer to apply state-of-the-art generative models to solve complex challenges in automotive engineering. This role focuses on creating intelligent agents that leverage generative capabilities for reasoning, planning, and executing complex tasks autonomously. The ideal candidate will bridge the gap between generative AIs creative potential and agentic AIs autonomous action, developing systems that can understand, reason, and act in dynamic environments.
Key Responsibilities
Integrated AI System Development:
Design and build AI agents that utilize large language models for reasoning and decision-making
Develop systems where generative AI components enable sophisticated planning and problem-solving
Create autonomous agents capable of using tools, APIs, and external systems through generative interfaces
Implement multi-agent systems where generative AI facilitates communication and collaboration
Generative AI Capabilities:
Fine-tune and optimize large language models for specific agentic tasks
Develop prompt engineering strategies for complex reasoning and chain-of-thought processes
Implement RAG (Retrieval-Augmented Generation) systems to enhance agent knowledge and context
Create generative models for code generation, content creation, and strategic planning within agent frameworks
Agent Architecture & Autonomy:
Build reflective agents that can critique and improve their own reasoning processes
Design goal-oriented systems that use generative AI for planning and adaptation
Implement memory architectures that allow agents to learn from experience and maintain context
Develop safety mechanisms and oversight for autonomous generative agents
Multi-Modal Agent Systems:
Integrate vision, language, and action capabilities within agent frameworks
Develop agents that can process and generate across multiple modalities (text, image, audio)
Create embodied agents that interact with digital and physical environments
Research & Innovation: Stay current with the latest academic research and open-source advancements in generative AI. Prototype new ideas and conduct experiments to validate their feasibility and impact.
Education: Ph.D in Computer Science, Electrical Engineering, Mechanical Engineering or related streams.
Technical Proficiency:
Experience with generative AI (LLMs, diffusion models, generative architectures)
Experience with agentic AI systems, reinforcement learning, or autonomous systems
Strong programming skills in Python and experience with AI/ML frameworks (PyTorch, TensorFlow)
Experience with LangChain, AutoGPT, Microsoft Autogen, or similar agent frameworks
Proficiency with transformer architectures and fine-tuning techniques
Deep understanding of prompt engineering, reasoning techniques, and LLM capabilities
Experience with RAG systems, vector databases, and knowledge retrieval
Knowledge of reinforcement learning, planning algorithms, and decision-making systems
Familiarity with multi-agent systems and emergent behavior
Ph.D
