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Zealogics.com

AI Fullstack Architect

Posted 4 months ago
5-10 years experience
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AI Summary

Lead the design and delivery of end-to-end AI fullstack solutions, integrating frontend interfaces with robust backend services and AI layers. Manage the full development lifecycle including rapid prototyping, MLOps implementation, and mentoring engineers through architecture reviews.

As an AI Fullstack Architect, you will spearhead the design and delivery of cutting-edge AI-driven solutions that span the entire technology stack—from intuitive frontend interfaces to robust backend services, sophisticated AI layers, and seamless deployment pipelines. Your expertise will be pivotal in shaping scalable, secure, and high-performance systems that integrate the latest advances in generative AI, machine learning, and computer vision, driving innovation and operational excellence across our AI initiatives.

In this role, you will architect and implement high-performance backend APIs using Python and FastAPI, while crafting responsive, modern frontend applications with React and Next.js. You will integrate generative AI capabilities such as large language models (LLMs), retrieval-augmented generation (RAG), prompt engineering, and fine-tuning, alongside traditional ML models and OpenCV-based computer vision solutions. Your responsibilities include driving rapid prototyping cycles to validate concepts swiftly, enforcing best practices in software architecture and MLOps, and implementing comprehensive automated testing strategies to ensure robust, maintainable codebases.

Day-to-day, you will collaborate closely with cross-functional teams—including data scientists, domain experts, product owners, and DevOps engineers—to deliver production-ready AI systems that meet stringent industrial standards for compliance, auditability, and reliability. You will lead Agile Scrum ceremonies, manage backlogs and sprints via Azure DevOps, and mentor engineers through architecture reviews and knowledge sharing, fostering a culture of technical excellence and continuous improvement.

You will need to mentor engineers, conduct architecture reviews, and champion technical excellence across the team. Ensure solutions meet stringent industrial requirements (compliance, auditability, reliability, low latency where required).

Joining our dynamic, innovation-driven environment, you will have the opportunity to influence the future of AI applications at scale, grow your leadership capabilities, and work alongside passionate experts committed to pushing the boundaries of technology. Our collaborative team structure emphasizes transparency, agility, and shared ownership, empowering you to make a tangible impact on transformative AI solutions that redefine industry standards.

Key Responsibilities • Architect and lead the design of end-to-end AI-fullstack solutions (frontend → backend → AI layer → deployment), ensuring scalability, security, performance, observability, and maintainability. • Design and implement high-performance backend APIs and services using Python + FastAPI. • Build modern, responsive, and performant frontend applications using React and Next.js. • Integrate and productionize Generative AI capabilities (LLMs, RAG, agents, prompt engineering, fine-tuning) alongside traditional ML models and OpenCV-based computer vision solutions. • Drive rapid prototyping cycles to validate ideas quickly and iterate based on stakeholder feedback. • Establish and enforce software architecture best practices, design patterns, microservices, clean architecture, and MLOps principles. • Implement comprehensive automated testing strategies with pytest (unit, integration, E2E) and maintain high code quality. • Lead and participate in Agile Scrum ceremonies; manage backlogs, sprints, and deliverables using Azure DevOps (Boards, Repos, Pipelines, Test Plans). • Collaborate closely with data scientists, domain experts, product owners, and DevOps teams to deliver production-ready AI systems. • Mentor engineers, conduct architecture reviews, and champion technical excellence across the team. • Ensure solutions meet stringent industrial requirements (compliance, auditability, reliability, low latency where required).

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