Senior AI Architect Product Engineering / SDLC

Position Overview

We are looking for a Senior AI Architect who can bridge product vision, AI engineering, software architecture, and rapid product development to dramatically reduce the time required to take ideas from concept to production.

The role is focused on building a high-velocity, AI-first product engineering capability, using modern AI-assisted development and vibe coding / AI coding agents to accelerate design, development, testing, and deploymentwithout compromising scalability, security, quality, or maintainability.

The ideal candidate will be a hands-on technology leader who can architect solutions, build prototypes, lead AI-assisted development, establish engineering standards, and mentor development teams.

Key Responsibilities

1. AI-First Product Development

- Lead the development of new products, features, and MVPs using AI-assisted software development and vibe coding approaches.
- Convert business/product requirements into working prototypes and production-ready solutions rapidly.
- Use AI coding agents and modern development tools to significantly reduce development lead time.
- Establish a prototype validate develop production model that enables faster product releases.
- Identify opportunities to use AI to automate repetitive development, testing, documentation, debugging, and deployment activities.

2. Solution & Product Architecture

- Define scalable, secure, modular, and cost-effective architectures for new products.
- Make pragmatic technology choices based on speed-to-market, maintainability, scalability, and business value.
- Design APIs, microservices, databases, cloud architecture, integrations, and AI/ML components.
- Create reusable architecture patterns, frameworks, components, and accelerators across products.
- Ensure AI-generated code and solutions follow enterprise architecture and engineering standards.

3. AI Coding & Engineering Productivity -

- Lead adoption of tools such as Claude Code, GitHub Copilot, Cursor, Codex, and other emerging AI development platforms.
- Develop best practices for effective AI-assisted coding / vibe coding.
- Create reusable prompts, coding standards, agent workflows, context repositories, and development playbooks.
- Establish approaches for AI agents to support requirements analysis, coding, testing, code review, documentation, and DevOps.
- Continuously evaluate emerging AI development technologies and integrate those that provide measurable productivity gains.

4. Rapid Prototyping & MVP Development

- Work closely with Product Managers, UX teams, business stakeholders, and engineering teams to rapidly validate ideas.
- Build functional prototypes and MVPs in days/weeks rather than traditional development cycles.
- Challenge requirements and simplify solutions wherever possible to improve speed and customer value.
- Establish a culture of "build fast,



validate rapid, learn fast."

5. Technical Leadership

- Provide technical leadership to architects, senior developers, and engineering teams.
- Mentor developers on AI-assisted development methodologies and modern software engineering practices.
- Define engineering guidelines for AI-generated and AI-assisted code.
- Conduct architecture and code reviews for critical solutions.
- Help teams overcome complex technical challenges and remove development bottlenecks.

6. Quality, Security & Governance

- Ensure rapid AI-assisted development does not compromise software quality, security, privacy, or compliance.
- Establish automated testing, code quality, security scanning, and CI/CD practices.
- Define guardrails for the responsible use of AI-generated code.
- Ensure appropriate human review and validation of AI-generated solutions.
- Drive automated quality gates wherever practical.

7. DevOps & Cloud Engineering

- Promote automated CI/CD and infrastructure-as-code practices.
- Leverage cloud-native technologies and serverless/containerized architectures where appropriate.
- Automate environments, deployments, testing, monitoring, and operational processes.
- Work with DevOps teams to establish highly automated product delivery pipelines.

Key Success Measures / KPIs

The primary objective of this role is reducing product development lead time while increasing engineering productivity and quality.

Success will be measured through:

- Reduction in idea-to-MVP time.
- Reduction in requirements-to-production lead time.
- Increase in development velocity through AI-assisted engineering.
- Percentage of development activities automated or AI-assisted.
- Reduction in engineering effort per feature/release.
- Increase in release frequency.
- Reduction in defects and rework.
- Adoption of AI coding tools across engineering teams.
- Number of reusable AI/product engineering accelerators created.
- Successful conversion of prototypes into production-ready products.
- Improvement in overall engineering productivity and cost efficiency.

Required Experience & Skills

- 12+ years of software engineering / architecture experience, with significant experience in product development.
- Strong experience as a Solution Architect, Technical Architect, Principal Engineer, or AI Architect.
- Proven experience building and launching software products.
- Strong hands-on programming experience in one or more contemporary languages such as Python, Java, JavaScript/TypeScript, C#, or Go.




- Strong understanding of modern web, API, cloud-native, microservices, and distributed architectures.
- Hands-on experience with Generative AI and AI coding assistants/agents.
- Practical experience with tools such as Claude Code, GitHub Copilot, Cursor, Codex, or equivalent AI development platforms.
- Strong knowledge of databases, APIs, cloud platforms, DevOps, CI/CD, and automated testing.
- Experience with AWS, Azure, or GCP.
- Strong understanding of software security, scalability, performance, and observability.
- Ability to rapidly understand unfamiliar codebases and use AI tools to accelerate development.
- Excellent problem-solving and technical decision-making skills.

Preferred Experience

- Experience building AI-powered products or SaaS platforms.
- Experience with LLMs, RAG, AI agents, vector databases, prompt engineering, and AI orchestration frameworks.
- Experience implementing agentic software development workflows.
- Experience establishing AI-assisted engineering practices across development teams.
- Experience taking products from concept/MVP to production at scale.
- Startup/product-company experience where speed and execution are critical.
- Experience with automated code generation, testing, documentation, and DevOps.
- Open-source contribution or experience evaluating emerging AI technologies.

Ideal Candidate Profile

We are looking for someone who is not just an architect who creates diagrams, but a hands-on AI-native technology leader who builds.

The ideal candidate:

- Thinks product first and technology second.
- Can turn an idea into a working prototype quickly.
- Is comfortable writing and reviewing code.
- Uses AI as a development partner and engineering multiplier.
- Understands when to use AI-generated code and when human engineering judgement is required.
- Can move between business requirements, product design, architecture, code, and deployment.
- Is highly curious about emerging AI technologies.
- Challenges traditional development approaches and continuously looks for ways to eliminate unnecessary effort.
- Has a strong "build it, test it, ship it" mindset.
- Can influence and enable an entire engineering organization to adopt AI-first development practices.

Education

Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related discipline.

Role Proposition

This is a high-impact role responsible for helping establish the organization's next-generation AI-native product engineering model.

The objective is not simply to use AI to write code faster, but to rethink the entire product development lifecycle—from idea, requirements, architecture, UX and coding through testing, deployment and operations—to achieve significantly faster time-to-market, higher engineering productivity, and lower overall product development cost.

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