We are seeking an experienced AI-SDLC Architect (FDE) to help enterprise engineering teams transform traditional software development lifecycles into AI-native, agentic development workflows. The ideal candidate will combine expertise in AI-powered software engineering, platform architecture, CI/CD, and stakeholder engagement to drive AI-assisted development at scale.
Key Responsibilities
- Architect and implement AI-native SDLC workflows, from specification-driven development through deployment.
- Design and deploy AI coding agent architectures integrated with enterprise development pipelines and cloud platforms.
- Define reference architectures, blueprints, and governance models for AI-assisted software delivery.
- Build scalable, secure CI/CD pipelines incorporating AI tooling, security controls, and quality gates.
- Collaborate directly with client engineering teams to drive adoption of agentic development practices.
- Provide technical leadership, design reviews, and best practices for AI-enabled software engineering.
Mandatory Requirements
- 12-15 years of experience in software engineering, architecture, or platform engineering.
- Hands-on experience with AI development tools such as GitHub Copilot, Copilot CLI, Claude Code, VS Code AI extensions, or equivalent.
- Experience implementing spec-driven development using SpecKit, OpenSpec, BMD, or similar frameworks.
- Strong understanding of AI artifact management, FinOps/token consumption management, and AI guardrails.
- Experience designing agent memory management (static, dynamic, and episodic memory) and multi-agent orchestration.
- Proven expertise in architecting AI-native software delivery platforms and developer workflows for enterprise environments.
- Strong knowledge of FastAPI (or equivalent API frameworks).
- Experience with Azure OpenAI, Amazon Bedrock, or similar AI gateway architectures.
- Expertise in Infrastructure as Code (Terraform) and CI/CD pipelines using GitHub Actions or equivalent tools.
Preferred Skills
- Experience as a Forward Deployed Engineer, Solution Architect, or Lead Engineer working directly with client teams.
- Strong Python development background.
- Experience with AI security, evaluation frameworks, observability, and monitoring.
- Knowledge of Docker, Kubernetes, cloud-native deployments, and enterprise security practices.
- Exposure to both AWS and Azure environments.