Live opening · Posted 6 hours ago

Dev AI Senior Architect

Chargebee · Chennai, Tamil Nadu, India (Hybrid)
Linkedin Hybrid
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At a glance

The key details from the original listing.

Posted 6 hours ago
CompanyChargebee
LocationChennai, Tamil Nadu, India (Hybrid)
Work modeHybrid
SkillsPython, Azure, GCP, Docker, Kubernetes
SourceLinkedin
Listed6 hours ago

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About the role

Description supplied by the original job listing.

About Chargebee
Chargebee is a leading provider of billing and monetization solutions, empowering businesses with recurring revenue models to streamline revenue and finance operations, capture actionable insights, and drive growth. Chargebee is trusted by businesses of all sizes, including Zapier, LegalZoom, Lambda, Freshworks, DeepL, Condé Nast, and Pret a Manger, and is proud to have been consistently recognized by customers as a Leader in Subscription Management on G2.
With headquarters in North Bethesda, Maryland, our team members are based primarily in India, the U.S., and Europe.
About the Team
The AI Center of Excellence at Chargebee is a multidisciplinary group of engineers and architects dedicated to scaling artificial intelligence across our product ecosystem. We focus on building robust, production-grade AI systems that enhance user experiences and streamline complex subscription workflows. Our team values technical excellence, iterative learning, and a collaborative approach to solving high-impact challenges in the B2B SaaS space.
About the role
As a Senior Principal Dev AI Architect, you will lead the design and implementation of sophisticated AI-powered tools and autonomous agents. You will be responsible for the end-to-end architecture of large-scale AI systems, ensuring they are performant, secure, and scalable for global enterprise needs. In this role, you will act as a technical authority, mentoring other engineers while collaborating with stakeholders to translate business requirements into innovative AI solutions.
What you'll do
• Architect and deploy production-grade LLM applications and agentic workflows that integrate seamlessly with enterprise SaaS platforms.
• Design and optimize Retrieval-Augmented Generation (RAG) pipelines to improve the accuracy and contextual relevance of AI outputs.
• Develop reusable AI engineering frameworks and assets to accelerate the adoption of generative AI across various engineering teams.
• Implement rigorous AI observability and monitoring systems to track model performance, latency, and drift in real-time.
• Lead the integration of complex APIs and data sources to power intelligent automation and
decision-making tools.
• Ensure all AI implementations adhere to strict security, privacy, and compliance standards, particularly regarding data handling and prompt injections.
• Collaborate with product and business leaders to define the long-term AI roadmap and identify high-value opportunities for automation.
• Provide technical mentorship and set the standard for code quality, documentation, and architectural best practices within the AI domain.
What you'll bring
• 7+ years of experience in software engineering with a focus on building and scaling complex distributed systems.
• 4+ years of experience specifically in AI/ML engineering, including hands-on development with Large Language Models (LLMs).
• Expert-level proficiency in Python and modern web frameworks such as FastAPI for building
high-performance APIs.
• Proven experience building and maintaining RAG pipelines and utilizing orchestration frameworks like LangChain or equivalent tools.
• Deep understanding of cloud infrastructure on platforms such as GCP, leading SaaS companies, or Azure, including containerization with Docker and Kubernetes.
• Strong background in data engineering, including experience with vector databases and managing large-scale datasets for AI training or inference.
• Experience implementing AI-specific security measures and ensuring compliance within highly regulated software environments.
• Bachelor's degree in Computer Science, Engineering, or a related technical field.
Nice to have
• Experience with advanced prompt engineering techniques and automated evaluation frameworks for LLM
outputs.
• Familiarity with fine-tuning open-source models for specific domain tasks in a B2B environment.
• Contributions to open-source AI projects or a history of technical leadership in AI-focused communities.
• Advanced knowledge of AI-native observability tools for debugging complex agentic interactions.

Work arrangement
Hybrid

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