DATA -Gen AI - Manager

8 - 13 years

30 - 35 Lacs

hyderabad

Posted:3 weeks ago| Platform:

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Skills Required

computer science business communication solution architecture assurance consulting machine learning data processing scrum monitoring python

Work Mode

Work from Office

Job Type

Full Time

Job Description

Provide strategic direction and technical leadership for AI initiatives, guiding the team in designing and implementing state-of-the-art AI solutions.
Lead the design and architecture of complex AI systems, ensuring scalability, reliability, and performance.
Drive the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI.
Collaborate with stakeholders to identify business opportunities, define AI project goals, and prioritize initiatives based on strategic objectives.
Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges.
Utilize generative AI techniques, such as LLMs, Agentic Framework to develop innovative solutions for enterprise industry use cases.
Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities.
Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment.
Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs.
Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs.
Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly.
Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency.
Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases.
Ensure compliance with data privacy, security, and ethical considerations in AI applications.
Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications.

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