Program Director

Job Requirements

The AI Engineering Architect will define and drive AI-enabled engineering practices across application development teams.Focus on establishing reusable AI engineering patterns, scaffolding, agents, skills, context management, guardrails, evaluation mechanisms to accelerate software delivery while maintaining engineering quality and governance.Should have strong fullstack architecture experience across frontend, backend, DevOps, and cloud technologies, combined with practical experience applying GenAI across software development lifecycle.Define the AI Engineering architecture and strategy for adopting GenAI across application development and maintenance.Architect AI-enabled development solutions across frontend and Python/Java/.NET/Node.js backend technology stacks.Establish engineering guardrails, security controls, code-quality standards, evaluation frameworks, and human-in-the-loop checkpoints for AI-generated artifacts.Collaborate with Enterprise Architects, Engineering Leads, DevOps, Cloud, Security, Product teams to integrate AI engineering practices into existing delivery processes.Define reusable reference implementations, templates, accelerators, agent/skill libraries, and engineering standards that can be adopted across multiple applications and technology stacks.Provide tech leadership for AI engineering adoption, including POCs, application onboarding, developer enablement, adoption metrics, productivity measurement, and continuous improvement.12+ yrs of software engineering experience, with significant experience in application architecture and tech leadership. Strong architecture knowledge across frontend technologies and backend technologies.Experience with GenAI, LLMs, coding agents, prompt/context engineering, agentic workflows..Experience implementing AI engineering scaffolding, reusable agents, developer accelerators, or SDLC automation preferred.Strong understanding of application architecture including APIs, microservices, event-driven architectures, databases, integration patterns, and distributed systems.Good knowledge of DevOps and CICD, including Git-based dev, build pipelines, automated testing, code quality, security scanning, deployment automation.Strong cloud exposure across AWS, Azure, or GCP, including containers and Kubernetes based deployment environments.Understanding of enterprise AI considerations including security, privacy, responsible AI, governance, observability, evaluation, and protection of enterprise data.

Work Experience

10-15Years