Job Description About the Role We are hiring a Principal Full Stack AI Engineer to lead the design, development, and delivery of our Generative AI platform. This is a hands-on technical leadership role — you will architect scalable AI systems, integrate LLMs into production environments, and set engineering standards for the team. You will collaborate across product, data science, and business teams to ship AI-powered solutions that create real impact. Key Responsibilities Drive Generative AI vision, strategy, and platform architecture end-to-end Design and build backend systems and APIs for AI-driven products and agentic frameworks Integrate Large Language Models (LLMs) into scalable, production-ready applications Establish reusable frameworks for model building, deployment, monitoring, and rollback Build guardrails, compliance rules, and approval workflows into the GenAI platform Implement monitoring, logging, tracing, and alerting across all AI systems Define peer review and validation processes for user-generated AI models and applications Create onboarding guides, templates, and sandbox environments for developers Mentor and upskill engineers; champion best practices across the team Communicate platform decisions and AI concepts clearly to non-technical stakeholders Requirements Required Skills & Experience 8+ years of professional software engineering experience in backend systems, APIs, or AI products Proficiency in Python (Django) and JavaScript / TypeScript (React) Strong understanding of REST / GraphQL APIs, backend architecture, and data modelling Hands-on experience with agentic AI frameworks — LangChain, LangGraph, Semantic Kernel, or similar Experience with containerisation (Docker) and cloud platforms (GCP, Azure, or equivalent) Solid working knowledge of SQL and NoSQL databases Proven track record of leading AI projects from conception to production deployment Strong system design skills and software engineering fundamentals Excellent communication and stakeholder management skills Good to Have Experience in a product, SaaS, or consulting environment Exposure to MLOps pipelines, big data, or deep learning workflows Prior experience building governance and compliance layers into AI systems
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