Deputy Director- AI/ML Full-time

Overview

Job Overview:

As an AI/ML Architect at PepsiCo’s Hyderabad location, you will be responsible for defining and implementing robust architectural patterns for Machine Learning (ML) models, Large Language Models (LLMs), AI agents, Digital Twin solutions and AI at the Edge architectures across the enterprise. You will collaborate closely with PepsiCo’s Data and AI Architecture team to ensure these patterns align with our Data and AI Strategy and technical standards. The role involves partnering with AI Observability teams to establish best practices for monitoring and observing ML models, computer vision systems, LLMs, and AI agents, ensuring they remain reliable, explainable, and performant.

Responsibilities

Responsibilities: Key Responsibilities

1. Architectural Pattern Definition: Define platform and model development patterns for ML models, LLMs, and AI agents in collaboration with the Data and AI Architecture team.
2. AI Observability: Establish and standardize AI observability patterns for ML, computer vision systems, LLMs, and AI agents to enable robust model monitoring, explainability, and drift detection.
3. Digital Twins & Edge AI: Develop architectural patterns for integrating Digital Twins and implementing AI at the Edge, facilitating their adoption in PepsiCo’s technology ecosystem.
4. Cross-Functional Collaboration: Engage with cross-functional teams (data science, engineering, operations, etc.) to standardize AI architecture practices and patterns across the enterprise.
5. Enterprise Alignment: Work closely with the Enterprise Architecture (EA) group to align AI/ML architecture patterns with PepsiCo’s broader IT strategy and standards.
6. Technology Evaluation: Evaluate and recommend AI/ML frameworks, platforms, and tools to ensure best-in-class solutions are utilized within PepsiCo’s AI ecosystem.
7. Scalability & Reliability: Ensure AI solutions are scalable, reliable, secure, and high-performing across cloud environments (Azure and AWS), following cloud-native architecture best practices.
8. Innovation & Strategy: Stay up to date with emerging AI/ML trends and technologies, and provide strategic technical direction for future AI initiatives and capabilities.

Qualifications

Qualifications:

• Experience: 10+ years of experience in AI/ML architecture, data science, or software engineering, with a focus on designing and deploying enterprise-scale AI solutions.
• Cloud Expertise: Strong expertise in Azure and AWS AI/ML services and cloud-native architectures for machine learning.
• Advanced AI Technologies: Hands-on experience with LLMs, AI agents, Machine Learning Operations (MLOps) pipelines, AI at the Edge deployments, and Digital Twin frameworks.
• AI Observability: Deep knowledge of AI observability (model monitoring, explainability, and drift detection) to ensure models remain trustworthy and effective over time.
• Programming & Frameworks: Strong programming skills in Python and experience with AI/ML frameworks such as TensorFlow, PyTorch, or similar libraries.
• IoT and Edge: Experience with IoT solutions and designing AI architectures for edge devices or edge computing environments.
• Collaboration: Ability to collaborate effectively with data engineers, data scientists/AI scientists, and IT leadership to drive AI initiatives forward.
• Governance & Compliance: Solid understanding of enterprise AI governance, security best practices, and compliance requirements (data privacy, model ethics, etc.).
• Proficiency in programming languages such as Python, R, or Java, with a focus on AI/ML applications.
• Experience with popular ML frameworks like TensorFlow, PyTorch, or scikit-learn.
• Knowledge of AI ethics and responsible AI practices.
• Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes).
• Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders.

Preferred Qualifications

• Education: Master’s or Ph.D. in Computer Science, AI/ML, or a related field.
• Industry Experience: Experience working in large-scale, AI-driven organizations or projects, preferably in a global enterprise setting.
• Certifications: Relevant certifications such as Microsoft Azure AI Engineer Associate, AWS Certified Machine Learning – Specialty, or TOGAF (Enterprise Architecture) certification.