Engineering Architect

Responsibilities

Leverage AI to accelerate architectural design and implementation of critical systems, ensuring scalability, resilience, and maintainability.

Utilize AI-powered tools for prototyping, generating foundational code, and modeling performance implications of different designs.

Develop prototypes, proof-of-concepts, and reference implementations to validate new technologies and establish architectural patterns.

Employ AI to rapidly build and iterate on prototypes, allowing for faster evaluation of new frameworks, libraries, and platforms.

Integrate AI into the development workflow to assist in debugging complex, distributed issues, analyzing performance bottlenecks, and suggesting optimized code refactoring.

Use AI to understand and navigate large, unfamiliar codebases quickly.

Establish and evangelize technical standards, frameworks, and best practices through direct contribution to shared libraries, core services, and example projects.

Automate the generation of documentation and code examples that align with established standards.

Enforce consistency and quality across multiple teams using AI-powered linters and code analysis tools.

Collaborate closely with product and engineering teams to translate requirements into actionable technical designs and executable code.

Employ AI-powered communication tools to distill complex technical trade-offs and rapidly create design documents and sequence diagrams that can be directly translated into development tasks.

Mentor and elevate the technical capabilities of senior engineers through pair programming, detailed code reviews, and direct technical collaboration.

Leverage AI to analyze the codebase for technical debt, complexity hotspots, and refactoring opportunities.

Model the potential impact and ROI of strategic technical initiatives using AI.

Own and implement critical pieces of infrastructure and cross-cutting concerns such as service discovery, authentication/authorization, and observability frameworks.

Accelerate the development of these core components, ensuring they are secure, efficient, and adhere to industry best practices.

Required Technical Skills

• Expert-Level Programming: Deep, hands-on expertise in one or more languages like Java, Go, or Python, with a proven ability to write clean, high-performance, and maintainable code.
• System Design & Implementation: Extensive experience designing, building, and operating large-scale distributed systems. Ability to translate architectural theory into practical, working code.
• Cloud-Native Technologies: Hands-on mastery of cloud platforms (AWS, Azure, GCP) and the container ecosystem (Kubernetes, Docker), including building and deploying production applications.
• Database Expertise: Deep practical knowledge of both relational (e.g., Oracle, PostgreSQL) and NoSQL databases (e.g., MongoDB), including data modeling and performance tuning for high-throughput systems.
• Infrastructure as Code (IaC): Proficiency in writing and maintaining complex IaC scripts using tools like Terraform or Ansible.
• API Design & Development: Proven experience building and scaling robust RESTful or gRPC-based APIs and services.
• Software Development Lifecycle: Strong command of the entire SDLC, including advanced Git workflows, CI/CD automation, and comprehensive testing strategies (unit, integration, end-to-end).
• AI Tools & Methodologies (Must): Demonstrable, in-depth experience using AI development tools (e.g., GitHub Copilot, Codex, Tabnine) as a primary part of the coding workflow.
• Strong understanding of AI/ML concepts, advanced prompt engineering, and integrating AI to solve complex implementation challenges, optimize algorithms, and accelerate debugging.
• A keen desire to push the boundaries of software development by applying AI to improve code quality, developer velocity, and system performance.

Recommended Qualifications

• 10+ years of relevant experience in software development, with a clear progression to a Principal, Staff, or senior architect role where coding was a primary responsibility.
• Demonstrated history of leading the development of complex, business-critical software projects from inception to launch.
• "Player-coach" mentality with a passion for both high-level architecture and hands-on implementation.
• Subject Matter Expert (SME) with deep implementation knowledge in several technology areas.
• Pragmatic approach to problem-solving and a bias for action.
• Exceptional ability to influence and lead technical direction through both written/verbal communication and direct code contribution.
• Consistently demonstrates clear and concise communication of complex technical topics to a wide range of audiences.