Gen AI Architect

PepsiCo; Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

A global leader in consumer goods is seeking a GenAI Technical Solutions Architect to enhance AI ML model deployment. The role involves developing GenAI architecture, leading strategy implementation, and collaborating with data scientists to drive digital transformation. Applicants should have over 10 years of experience in AI, proficiency in cloud technologies like Azure, and strong skills in NLP/AI software development. This position offers a dynamic environment focused on innovation and technology integration.

Qualifications

• Proven track record in shipping products and developing state-of-the-art Gen AI product architecture.
• 10+ years of experience with a strong balance of business acumen and technical expertise in AI.
• 5+ years in building and releasing NLP/AI software, with specific experience in RAG, agent systems and embedding models.
• Experience delivering Gen AI products including multi‑modal LLMs and foundation models.

Responsibilities

• Develop cutting-edge architectural strategies for Gen AI components and platforms.
• Lead working sessions for alignment and develop component reuse strategies.
• Implement LLM operations focusing on model performance and scalability.
• Collaborate with data scientists to pilot use cases aligned to business ROI and transformation.

Skills

NLP/AI software development

Gen AI product architecture

Communication skills

Cloud technologies (Azure)

DevOps principles

PyTorch

Education

Bachelor’s or Master’s degree in computer science or related field

Tools

PyTorch

TensorFlow

Kubernetes

Docker

LlamaIndex

LangChain

Job description

Overview

Overview: The GenAI Technical Solutions Architect develops and implements GEN AI architecture strategies, best practices, and standards to enhance AI ML model deployment and monitoring efficiency. Develop architecture roadmap and strategy for GenAI Platforms and tech stacks. This role will focus on the technical development, deployment, Code reuse and optimization of GenAI solutions, ensuring alignment with business strategies and technological advancements.

Responsibilities

Responsibilities:

1. 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. Balance build versus buy decisions, ensuring alignment with SaaS models and decision trees, particularly for the PepGenX platform. Emphasize low coupling and cohesive model development.
2. Lead working sessions for Arch Alignment, pattern library development, GEN AI Tools Data Architect alignment, tag new components to reuse (components reuse strategy), patterns of the use cases, reuse components (save efforts, time money).
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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.
11. 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.
12. Proactively reduce biases in model predictions, focusing on fair and inclusive AI systems through advanced debiasing techniques in embeddings and LLM training.
13. 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.

Qualifications

Experience:

1. Proven track record in shipping products and developing state‑of‑the‑art Gen AI product architecture.
2. 10+ years of experience with a strong balance of business acumen and technical expertise in AI.
3. 5+ years in building and releasing NLP/AI software, with specific experience in RAG, agent systems and embedding models.
4. Demonstrated experience in delivering Gen AI products, including multi‑modal LLMs, foundation models, and agentic AI systems.
5. Deep familiarity with cloud technologies, especially Azure, and experience deploying models for large‑scale inference using advanced LLM ops techniques.
6. Proficiency in PyTorch, TensorFlow, Kubernetes, Docker, LlamaIndex, LangChain, LLM, SLM, LAM, and cloud platforms, with a focus on implementing RAG and embedding pipelines.
7. Excellent communication and interpersonal skills, with a strong design capability and ability to articulate complex AI concepts to diverse audiences.
8. Hands‑on experience with chunking strategies, RAG implementation, and optimizing embedding models for various AI applications.

Qualifications:

1. Bachelor’s or Master’s degree in computer science, Data Science, or a related technical field.
2. Demonstrated ability to translate complex technical concepts into actionable business strategies. Experience in data‑driven decision‑making processes.
3. Strong communication skills, with the ability to collaborate effectively with both technical and non‑technical stakeholders.
4. Proven track record in managing and delivering AI/ML projects, with a focus on GenAI solutions, in large‑scale enterprise environments.