Role Overview
This role is designed for a practitioner who has evolved from deep experience in Artificial Intelligence into hands-on, production-grade software development using AI-assisted methodologies. The individual is expected to architect, build, and deliver robust, scalable products by leveraging AI not merely as a support tool, but as a core development paradigm.
In addition to technical excellence, this role carries a strong leadership mandate—to institutionalize AI-driven development practices and actively elevate the capabilities of the broader engineering team.
Key Responsibilities
- AI-Native Product Development
- Design and deliver end-to-end software solutions using AI-assisted development workflows.
- Translate business problems into scalable system architectures and working products.
- Own delivery from concept → prototype → production.
- AI-Assisted Engineering Practices
- Use advanced AI tools (LLMs, agents, code generation systems) to accelerate development while maintaining code quality and architectural integrity.
- Establish patterns for prompt engineering, agent orchestration, and reusable AI-driven workflows.
- Ensure generated code adheres to best practices in modularity, performance, and security.
- System Architecture & Design
- Define backend, frontend, and data architectures for modern applications (web, SaaS, enterprise systems).
- Design APIs, data models, and workflows optimized for AI-augmented systems.
- Integrate AI components (NLP, CV, predictive models) into production-grade systems.
- Engineering Governance
- Enforce code quality standards, version control discipline, testing strategies, and CI/CD pipelines.
- Review and refine AI-generated code to meet production standards.
- Establish guardrails for reliability, observability, and maintainability.
- Rapid Prototyping & Iteration
- Build functional prototypes at high velocity using AI tools.
- Iterate quickly based on stakeholder feedback and evolving requirements.
- Balance speed with long-term scalability and technical debt management.
- AI Strategy & Enablement
- Define how AI can be systematically leveraged across engineering workflows.
- Evaluate and integrate emerging AI tools and frameworks into the development stack.
- Drive adoption of AI-native development practices across teams.
Leadership & Capability Building
- Team Enablement
- Mentor engineers in adopting AI-assisted development workflows effectively and responsibly.
- Conduct hands-on sessions, code walkthroughs, and live builds to demonstrate best practices.
- Enable teams to move from ad-hoc AI usage to structured, repeatable engineering approaches.
- Upskilling & Knowledge Transfer
- Design internal playbooks, templates, and reusable patterns for AI-driven development.
- Create documentation and training material to standardize practices across teams.
- Act as a multiplier—raising the overall productivity and capability of the engineering organization.
- Technical Leadership
- Lead by example through high-quality implementations and disciplined engineering practices.
- Influence architectural decisions and guide teams on trade-offs between speed and scalability.
- Foster a culture of experimentation balanced with accountability and production readiness.
Required Qualifications
- Experience
- 10+ years in AI / Machine Learning / Data Science or related domains.
- Recent, hands-on experience building production software using AI-assisted coding tools.
- Demonstrated track record of delivering real-world products (not just prototypes).
- Technical Expertise
- Strong proficiency in modern programming languages (e.g., JavaScript/TypeScript, Python, or similar).
- Experience with backend frameworks (Node.js, Express, FastAPI, etc.) and modern frontend stacks.
- Solid understanding of databases (SQL), APIs, and distributed systems.
- AI Engineering Capability
- Deep familiarity with LLMs, prompt engineering, and agent-based systems.
- Experience integrating AI models into applications (APIs, pipelines, inference systems).
- Understanding of AI limitations, evaluation, and reliability considerations.
- Software Engineering Fundamentals
- Strong grasp of system design, scalability, and performance optimization.
- Experience with DevOps practices: CI/CD, containerization, cloud environments.
- Ability to write clean, maintainable, and testable code—even when AI-generated.
Preferred Qualifications
- Experience building internal AI tooling, developer platforms, or automation systems.
- Familiarity with multi-agent orchestration frameworks and workflow engines.
- Exposure to enterprise or government-grade systems with high reliability requirements.
- Prior experience in mentoring teams or leading engineering initiatives.
Key Traits
- Builder & Leader: Ships products while uplifting the team.
- AI Fluent: Uses AI as a core engineering multiplier with discipline.
- Teacher Mindset: Actively shares knowledge and builds team capability.
- Systems Thinker: Understands end-to-end architecture and trade-offs.
- Ownership Driven: Accountable for outcomes, not just outputs.
Success Criteria
- Deliver production-ready systems at significantly accelerated timelines using AI.
- Establish and scale AI-assisted development practices across teams.
- Measurably improve team productivity and engineering quality through upskilling.
- Create a self-sustaining engineering culture that effectively leverages AI.
