AI Architect

3 weeks ago

Hyderabad, Telangana, India PepsiCo Full time
Job Title: AI Architect - Gen AI Innovation

Overview: As an AI Architect at PepsiCo, you will be responsible for developing cutting-edge architectural strategies for Gen AI components and platforms. You will leverage advanced techniques such as chunking, Retrieval-Augmented Generation (RAG), AI agents, and embeddings to balance build versus buy decisions, ensuring alignment with SaaS models and decision trees, particularly for the PepGenX platform.

Responsibilities:

  • Develop cutting-edge architectural strategies for Gen AI components and platforms, leveraging advanced techniques such as chunking, Retrieval-Augmented Generation (RAG), AI agents, and embeddings.
  • Lead working sessions for architectural alignment, pattern library development, Gen AI tools data architect alignment, tag new components to reuse (components reuse strategy), patterns of use cases, reuse components (save efforts, time, money).
  • Lead the implementation of LLM operations, focusing on optimizing model performance, scalability, and efficiency. Design and implement LLM agentic processes to create autonomous AI systems capable of complex decision-making and task execution.
  • Work closely with data scientists and AI professionals to identify and pilot innovative use cases that drive digital transformation. Assess the feasibility of these use cases, aligning them with business objectives, ROI, and leveraging advanced AI techniques.
  • Gather inputs from various stakeholders to align technical implementations with current and future requirements. Develop processes and products based on these inputs, incorporating state-of-the-art AI methodologies.
  • Define AI architecture and select suitable technologies, with a focus on integrating RAG systems, embedding models, and advanced LLM frameworks. Decide on optimal deployment models, ensuring seamless integration with existing data management and analytics tools.
  • Audit AI tools and practices, focusing on continuous improvement of LLM ops and agentic processes. Collaborate with security and risk leaders to mitigate risks such as data poisoning and model theft, ensuring ethical AI implementation.
  • Stay updated on AI regulations and map them to best practices in AI architecture and pipeline planning. Develop expertise in ML and deep learning workflow architectures, with a focus on chunking strategies, embedding pipelines, and RAG system implementation.
  • Apply advanced software engineering and DevOps principles, utilizing tools like Git, Kubernetes, and CI/CD for efficient LLM ops. Collaborate across teams to ensure AI platforms meet both business and technical requirements.
  • Spearhead the exploration and application of cutting-edge Large Language Models (LLMs) and Generative AI, including multi-modal capabilities and agentic processes. Oversee MLOps, automating ML pipelines from training to deployment with a focus on RAG and embedding optimization.
  • Engage in sophisticated model development from ideation to deployment, leveraging advanced chunking and RAG techniques. Effectively communicate complex analysis results to business partners and executives.
  • Proactively reduce biases in model predictions, focusing on fair and inclusive AI systems through advanced debiasing techniques in embeddings and LLM training.
  • Design efficient data pipelines to support large language model training and inference, with a focus on optimizing chunking strategies and embedding generation for RAG systems.

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