Generative AI Researcher

Tata Consultancy Services; Warren (MI); On-site; USD 90,000 - 130,000; Full time

Job summary

Tata Consultancy Services seeks an Entry Level - Generative AI Engineer to apply state-of-the-art generative models for automotive engineering challenges. You will build intelligent agents that reason, plan, and act autonomously in dynamic environments.

Ideal candidates have Ph.D. in a related field, strong Python skills, and hands-on experience with LLMs, PyTorch/TensorFlow, and agent frameworks to enable tool use and collaboration.

Qualifications

• Ph.D in Computer Science, Electrical Engineering, Mechanical Engineering or related streams.
• Experience with generative AI, LLMs, diffusion models.
• Proficiency in Python and AI/ML frameworks (PyTorch, TensorFlow).
• Experience with LangChain, AutoGPT, Microsoft Autogen, or similar agent frameworks.

Responsibilities

• 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.

Skills

Generative AI (LLMs & diffusion)

Agentic AI systems & RL

Python programming

AI/ML frameworks (PyTorch, TensorFlow)

LangChain / AutoGPT / Autogen

Transformer architectures & fine-tune

Prompt engineering & reasoning

RAG systems & vector DBs

Multi-agent systems

Education

Ph.D in Computer Science / Electrical / Mechanical Engineering or related fields

Tools

PyTorch

TensorFlow

LangChain

AutoGPT

Microsoft Autogen

Job description

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 AI's creative potential and agentic AI's 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

Qualifications

• Ph.D

Benefits

Yes