AI Architect

Job description

Must Have Skills Strong experience in enterprise solution architecture, backend engineering, and distributed systems design. Expertise in Python, TypeScript, Node.js, Java, Go, or similar backend technologies. Strong experience designing APIs, integration platforms, event-driven architectures, and workflow orchestration systems. Hands-on experience with GenAI applications, AI agents, RAG workflows, tool-calling systems, and enterprise chatbot platforms.

Strong understanding of LLM ecosystems including OpenAI, Claude, ChatGPT Enterprise, Azure OpenAI, Amazon Bedrock, Google Vertex AI, Databricks, or similar AI platforms. Experience designing orchestration frameworks, agent runtimes, workflow engines, and stateful task execution systems. Strong understanding of prompt engineering, structured outputs, guardrails, function calling, and AI safety controls. Experience integrating enterprise collaboration and workflow platforms including Slack, Jira, Zendesk, ServiceNow, and Confluence.

Strong understanding of enterprise identity and access management including Okta, SCIM, SSO, RBAC, ABAC, secrets management, and governance controls. Experience with observability platforms such as OpenTelemetry, monitoring frameworks, logging, tracing, metrics, and operational dashboards. Strong knowledge of secure architecture practices, enterprise governance, data privacy, compliance, and operational reliability. Experience with CI/CD pipelines, DevOps practices, deployment automation, rollback strategies, and operational lifecycle management.

Strong stakeholder management and leadership skills across architecture, engineering, AI Ops, governance, security, and business teams.

Key Responsibilities

Design and architect enterprise-grade AI and backend solutions supporting scalable, reliable, and secure distributed systems. Lead the development of APIs, integration platforms, and event-driven architectures to support enterprise workflows and automation. Drive the design and implementation of GenAI-powered applications, AI agents, and RAG-based systems for enterprise use cases. Define and implement LLM orchestration frameworks, agent runtimes, and workflow engines to support complex AI-driven operations.

Establish best practices for prompt engineering, guardrails, structured outputs, and AI safety controls across applications. Integrate enterprise platforms such as Slack, Jira, Zendesk, ServiceNow, and Confluence with backend systems and AI services. Design and enforce identity and access management solutions using Okta, SCIM, SSO, RBAC, ABAC, and secrets management. Implement observability solutions including logging, monitoring, tracing, and metrics for AI and backend platforms.

Ensure adherence to security, governance, compliance, and data privacy standards across all systems and integrations. Build and maintain CI/CD pipelines and DevOps processes for efficient deployment, automation, and lifecycle management. Collaborate with cross-functional stakeholders across architecture, engineering, AI Ops, governance, and business teams to align solutions with organizational goals. Provide technical leadership, mentorship, and guidance to engineering teams in building scalable AI and backend systems.