Principal AI Architect

In this role, you’ll define the company’s architectural vision, establish engineering standards, and drive technical decisions across multiple R&D teams. You’ll work closely with Engineering, Product, DevOps, AI, Security, and Data teams to build scalable, secure, and high-performing solutions while evaluating and adopting cutting-edge AI technologies.

This is a highly strategic and hands-on role, ideal for someone who enjoys solving complex technical challenges, mentoring engineering teams, and shaping the future of modern AI products.

Responsibilities

• Define and lead the architectural vision across the organization.
• Design scalable, secure, and high-performance distributed systems.
• Establish architecture standards, best practices, and technical guidelines across multiple engineering teams.
• Design and lead enterprise authentication, authorization, SSO, Groups, Policies, and identity management solutions.
• Architect advanced GenAI solutions, including RAG, Embeddings, Vector Databases, LLM integrations, AI Agents, Guardrails, and large-scale inference workloads.
• Define reference architectures and engineering standards for deployment, monitoring, observability, reliability, and security.
• Drive architectural decisions around scalability, performance, availability, security, and operational efficiency.
• Own the architectural roadmap while balancing business value, operational costs (COGS), scalability, and long-term maintainability.
• Lead Proofs of Concept (POCs) to evaluate emerging AI technologies, frameworks, and developer productivity tools.
• Promote engineering excellence through CI/CD best practices, observability, monitoring, security, and compliance.
• Collaborate closely with Engineering, Product, AI, DevOps, Security, Data teams, customers, and strategic partners.

Requirements

• 10+ years of software development experience, including 4+ years in architecture or technical leadership roles.
• Proven experience designing large-scale distributed systems running on Kubernetes.
• Strong experience with AWS, Azure, or GCP.
• Experience designing cloud-native architectures, microservices, serverless applications, and data pipelines.
• Hands-on experience with Docker, Kubernetes, and CI/CD pipelines.
• Experience designing and deploying On-Premise / Air-Gapped environments.
• Practical experience working with Generative AI technologies, including LLMs, RAG, Embeddings, Vector Databases, AI Agents, MCP, or similar AI frameworks.
• Strong understanding of observability, monitoring, and performance optimization.
• Experience implementing security and compliance best practices, including IAM, Secrets Management, and infrastructure hardening.
• Excellent architectural thinking, problem-solving, mentoring, and technical leadership skills.
• Strong communication and collaboration skills, with the ability to influence cross-functional teams.

Preferred Qualifications

• Hands-on software development experience with Python, TypeScript, Node.js, or similar languages.
• Experience deploying SaaS and On-Premise enterprise products.
• Experience with Prometheus, Grafana, ELK, or similar observability platforms.
• Experience with GPU optimization, AI inference infrastructure, or large-scale AI workloads.
• Experience working in fast-growing AI startups or enterprise software companies.