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Generative AI Solutions Architect
2 months ago
We are looking for an accomplished Generative AI Architect who possesses a strong foundation in both Data Science and Software Engineering. This role is pivotal in spearheading the design, development, and implementation of innovative AI solutions at HCLTech. You will take charge of leading a dedicated team of Generative AI ML Engineers to construct a robust multi-agent framework.
Key Responsibilities:
- Engage with diverse teams, including User Teams, Data Scientists, Core Development Teams, and Partner Developers, to ascertain requirements and oversee the architecture, development, and testing of AI agents.
- Design and implement engineering solutions and generative AI applications, ensuring they are scalable, efficient, and maintainable.
- Assist in the formulation of the product roadmap and define comprehensive product requirements.
- Architect and design extensive enterprise-scale frameworks, such as Knowledge Graphs.
- Produce clean, high-performance, and maintainable code.
- Develop and implement large-scale clustering and topic management systems.
- Utilize prompt engineering techniques to refine and enhance Large Language Models (LLMs) for improved performance tailored to specific applications.
- Stay updated with the latest advancements in Generative AI application development and contribute actively to the research and development of new applications.
Qualifications:
- A Master's or Ph.D. in Computer Science or a related discipline.
- A minimum of 10 years of experience, with at least 5 years focused on designing and building ML/AI Solutions.
- Demonstrated experience in a significant technology startup or AI software product, leading the development and adoption of products or platforms.
- Strong expertise in programming languages such as Java, Python, and familiarity with ML platforms and LLMs.
- Experience in constructing graph-based systems.
- Proven track record in architecting and leading the development of extensive generative AI systems or platforms.
- Experience in developing technical products or platforms for large enterprises.
- Solid understanding of software engineering best practices, including version control systems (e.g., Git), code reviews, and testing methodologies.
- Familiarity with large language models (LLMs) and prompt engineering techniques; experience with vector databases is a plus.
- Excellent communication skills to effectively collaborate and present findings to both technical and non-technical stakeholders.
- Proven ability to quickly adapt and learn new technologies and frameworks.
- A proactive mindset with a strong commitment to continuous learning and research.
