AI/ML Solution Architect

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