Senior Generative AI Architect

1 week ago

Noida, Uttar Pradesh, India TETRAHED INC Full time
Job Title : Senior Generative AI Architect

Job Summary :

Tetrahed Inc is seeking a highly skilled Senior Generative AI Architect to lead the development of advanced technical solutions that integrate cutting-edge generative AI technologies into cloud-native architectures. As a key member of our team, you will be responsible for designing and implementing GenAI-driven applications that enhance system functionality and scalability.

Key Responsibilities :

- Develop solutions leveraging GenAI frameworks and libraries to integrate advanced AI capabilities into cloud-native architectures.

- Lead the design and implementation of GenAI-driven applications, ensuring seamless integration with microservices and container-based environments.

- Create solutions that fully leverage the capabilities of modern microservice and container-based environments running in public, private, and hybrid clouds.

- Collaborate on joint technical projects with partners, including Google, Microsoft, AWS, IBM, Red Hat, Intel, Cisco, and Dell/VMware.

- Develop innovative GenAI solutions from ideation to MVP, ensuring high performance and reliability within cloud-native frameworks.

Required Skills :

- A deep understanding of modern technical and cloud-native practices, AI, DevOps, and machine learning technologies, particularly in generative models.

- Expertise in GenAI frameworks, including LangChain, OpenAI API, Gemini, and Hugging Face Transformers.

- Strong understanding of NLP techniques and tools, including tokenization, embeddings, transformers, and language models.

- Proven experience developing complex solutions that leverage cloud-native technologies, featuring container-based, microservices-based approaches.

- Strong leadership skills and the ability to inspire and manage teams.

Preferred Skills :

- Machine Learning Operations (MLOps) experience, including deploying, monitoring, and maintaining AI models in production environments.

- Data engineering for AI, including data preprocessing, feature engineering, and creating pipelines to feed AI models with high-quality data.

- AI model fine-tuning, including fine-tuning pre-trained models on specific datasets to improve performance for specialized tasks.

- AI ethics and bias mitigation, including knowledge of ethical considerations in AI and experience implementing strategies to mitigate bias in AI models.

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