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Entrylevel Generative Ai Product Management Jobs

Centillion Infotech

AI Lead (Generative AI)

Tampa, FL · On-site

$127K - $156K/yr

Design and implement solutions leveraging LLMs and Generative AI . * Assist with deployment, monitoring, maintenance, and optimization of AI models in production environments. * Collaborate with Data ...

Uncountable

Platform Engineer - Generative AI

New York, NY · On-site

$120K - $160K/yr

... engineer, product manager, and whatever else is needed to create an amazing product. This is a ... Strong interest in Generative AI * Solid computer science and software engineering fundamentals ...

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How much do entrylevel generative ai product management jobs pay per year?

As of Sep 7, 2026, the average yearly pay for entrylevel generative ai product management in the United States is $230,997.00, according to ZipRecruiter salary data. Most workers in this role earn between $233,000.00 and $244,000.00 per year, depending on experience, location, and employer.
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Entrylevel Generative Ai Product Management jobs near you
Infographic showing various Entrylevel Generative Ai Product Management job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $230,997 per year, or $111.1 per hour.

Generative AI Researcher

Tata Consultancy Services

Warren, MI

Full-time

Re-posted 19 days ago

Tata Consultancy Services rating

6.5

Company rating: 6.5 out of 10

Based on 21 frontline employees who took The Breakroom Quiz

176th of 226 rated it services

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

Ph.D

What Tata Consultancy Services employees say

Pay

Benefits

Hours and flexibility

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