AI-Native Technical Architect
- Pride Veteran Staffing, Inc.
10 Hours Ago
NA
C2C
Remote
6-10 Years
Required Skills: Java, Spring Framework, Spring Boot, CI/CD pipelines, DevOps, monitoring, logging, tracing
Job Description
Client is seeking a highly experienced, hands-on AI-Native Technical Architect II to lead application and solution architecture for large-scale enterprise applications. The ideal candidate will combine strong Java development and database expertise with deep knowledge of microservices, distributed systems, cloud-native architecture, and practical AI-assisted software engineering.
This role requires an architect who can translate business requirements into scalable technical solutions while remaining actively involved in implementation, code-level reviews, technical problem-solving, and engineering best practices.
The candidate will also help advance AI-native engineering practices by leveraging AI coding assistants, agent-based development workflows, code generation, and AI-assisted testing while ensuring security, quality, maintainability, and architectural consistency.
Key Responsibilities
- Design and implement scalable architectures for enterprise applications, backend services, workflows, and APIs.
- Provide hands-on technical leadership across Java-based application development and modern software engineering practices.
- Design and optimize SQL and NoSQL database solutions for large data volumes and high-performance applications.
- Define microservices architecture, service decomposition, API standards, and enterprise integration patterns.
- Architect distributed and real-time systems with a focus on scalability, reliability, performance, and fault tolerance.
- Incorporate AI-assisted development tools, AI copilots, and agent-based engineering workflows into the software development lifecycle.
- Leverage AI for code generation, automated testing, design assistance, and engineering productivity improvements.
- Review and validate AI-generated code to ensure security, quality, maintainability, and compliance with architectural standards.
- Establish appropriate guardrails and governance for AI-enabled engineering workflows.
- Drive cloud-native architecture, CI/CD automation, DevOps, observability, and modern SDLC practices.
- Collaborate with product managers, business stakeholders, engineering teams, and enterprise architects to deliver technical solutions.
- Mentor engineers, conduct architecture and code reviews, and promote engineering best practices.
- Consider security, scalability, reliability, regulatory requirements, cost, and long-term platform evolution when making architectural decisions.
Required Technical Skills
- 6+ years of experience designing and building enterprise web applications, backend services, workflows, and service-oriented solutions.
- Strong hands-on experience with Java, Spring Framework/Spring Boot, or comparable modern application technologies.
- Excellent SQL and relational database knowledge, including database design, query optimization, and performance tuning.
- Experience with NoSQL databases and data-intensive application architectures.
- Strong understanding of microservices, distributed systems, API design, service decomposition, and enterprise integrations.
- Experience designing applications that handle large data volumes and real-time processing requirements.
- Strong knowledge of cloud-native architecture and cloud platforms.
- Experience with CI/CD pipelines, DevOps, monitoring, logging, tracing, and observability.
- Demonstrated ownership of application architecture and solution design.
- Strong technical leadership, architecture governance, problem-solving, and communication skills.
AI-Native / AI-Assisted Engineering Experience
Candidates must have practical, professional experience using AI to improve software engineering and delivery. Relevant experience includes:
- AI-assisted coding and software development using tools such as GitHub Copilot or comparable AI coding assistants.
- AI agents and agent-based software engineering workflows.
- AI-assisted code generation, debugging, refactoring, and automated testing.
- Orchestrating AI agents across development and engineering workflows.
- Developing reusable AI skills and intelligent engineering processes.
- Reviewing and validating AI-generated code and technical designs.
- Implementing guardrails to maintain code quality, security, reliability, and architectural consistency.
- Demonstrating measurable improvements in engineering productivity, software quality, or delivery outcomes through AI adoption.
