Cloud Agentic AI Platform Engineer / Architect

• 5 years strong experience designing and building enterprise-scale AI platforms, including agent runtimes, orchestration engines, developer frameworks, and platform services.
• 5 years of expertise in cloud-native architecture, Kubernetes/OpenShift, microservices, APIs, event-driven systems, and scalable distributed platforms.
• 5 years of hands-on experience with hyperscaler AI offerings such as Google ADK / Agent Engine, Vertex AI, Azure AI Foundry, AWS Bedrock Agents, MCP ecosystems.
• 5 years of deep understanding of LLMs, reasoning models, tool calling, agent frameworks, RAG architectures, model evaluation, prompt engineering, inference optimization.
• 5 years of experience building reusable SDKs, developer platforms, low-code/no-code solutions, workflow automation capabilities, and self-service experiences.
• 5 years of strong DevOps and Platform Engineering expertise, including CI/CD, GitOps, Terraform, containerization, release automation, monitoring and observability, production operations.
• 5 years of experience with distributed data technologies including MongoDB, Redis, vector databases, caching platforms, session state management, high-performance AI data access patterns.
• 5 years of knowledge of AI platform governance, observability, security, guardrails, identity management, resiliency, and enterprise production operations.
• 5 years of strong software engineering skills in Python and/or Java.
• 5 years of demonstrated experience leading architecture, technical design, and engineering decisions for complex enterprise platforms.

Preferred Qualifications:-

• Experience building enterprise AI platforms in regulated environments.
• Knowledge of multi-agent architectures and autonomous workflow systems.
• Experience deploying and operating AI solutions across multi-cloud environments.
• Familiarity with enterprise AI governance frameworks and responsible AI practices.
• Experience building internal developer platforms and platform-as-a-product initiatives.
• Exposure to MLOps, LLMOps, and AI lifecycle management tools and processes.

Experience Level:-

• 10 years of software engineering, cloud platform engineering, or platform architecture experience.
• 5 years leading large-scale cloud-native platform initiatives.
• Proven experience designing and delivering production-grade AI, GenAI, or AI platforms at enterprise scale.