Generative AI Engineer

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This role involves working on real-world business problems by designing, developing, and deploying multi agent GenAI systems that operate concurrently to solve complex tasks at scale. 

Duties and Responsibilities:

• Design, develop, and implement state-of-the-art generative models for various applications, such as text generation, image synthesis and content creation.

• Implement Agentic AI architectures using frameworks such as LangGraph, LangChain, and custom orchestration logic.

• Experience working on open source LLMs like Llama, Falcon, BERT, Mistral etc., RAG systems and prompt engineering

• Pre-process and analyze large datasets to extract meaningful features and patterns suitable for training generative models.

• Optimize and fine-tune generative models for efficiency, scalability, and improved performance.

• Deploy and manage AI solutions on cloud platforms (AWS / Azure), following best practices.

• Implement strategies for reducing model latency and resource consumption.

• Develop and implement robust evaluation metrics to assess the performance and quality of generative models. • Architecture design and building scalable production systems

• Document code, models, and methodologies thoroughly to facilitate knowledge sharing and future development.

• Stay updated with the latest advancements in generative models, including GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and other relevant techniques.

• Stay at the forefront of the latest research papers and publications in the field of generative AI.

• Contribute to the company's intellectual property by researching and developing novel approaches to address specific business challenges.

Requirements:

• B Tech/M Tech in Computer/IT or related field from reputed engineering colleges

• 2-5 years of hands-on experience in ML domain.

• Practical experience with Generative AI & LLMs, Agentic AI and multi-agent systems, LangGraph, LangChain, Hugging Face, OpenAI APIs, RAG architecture and vector databases.

• Proven experience in developing generative models, with expertise in deep learning frameworks such as TensorFlow or PyTorch.

• Strong programming skills in languages like Python and proficiency in machine learning libraries.

• Solid understanding of neural network architectures, optimization techniques, and training methodologies

• Work experience on cloud platforms like AWS/Azure is desired.