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.