Full Stack AI Engineer

Responsibilities:

• Research and Experimentation: Continuously evaluate emerging AI models (OpenAI GPT, Anthropic Claude, Google Gemini, Qwen, GLM), agent frameworks (Google ADK, LangGraph), and tools (Vector DBs, Agent Observability, Prompt Optimisation, Agent Evaluations, Agent Memory) to identify practical opportunities for innovation.
• Architecture and Implementation: Design and implement scalable Agentic AI integrations within existing business processes and software, ensuring reliability, performance, and data privacy.
• Prototyping and R& D: Build proofs-of-concept and pilot features to validate new approaches from prompt engineering and fine-tuning to multi-agent orchestration.
• Technical Leadership: Mentor engineers across the organisation on AI utilisation best practices, lead architecture reviews, and contribute to shared internal libraries and model-integration frameworks.
• Industry Awareness and Advocacy: Track and evaluate industry trends, tools, and research; represent the company's AI engineering direction through internal knowledge-sharing and occasional external engagement.

Requirements:

• 10+ years in software engineering with deep technical fluency in the development, design, and architecture of modern full-stack web applications.
• Strong foundation to support the full stack, including front-end, back-end, databases (relational and NoSQL), API design, microservices, caching, performance tuning, security best practices, and common software design patterns.
• 1+ years of hands-on experience working with frontier LLMs and building AI agents.
• Demonstrated technical leadership and mentorship across an engineering department, with strong strategic thinking and problem-solving.
• Strong proficiency in Python, TypeScript, or similar, with experience integrating model APIs and building AI-enabled features.
• Working knowledge of AI Agent architecture, AI Agent patterns, prompt engineering, context engineering, and RAG.
• Familiarity with AI-enhanced developer workflows and Agentic AI IDEs (e. g., Cursor, Cline, Replit), for code generation, documentation assistants, or autonomous agents.
• A passion for continuous learning, experimentation, and bringing emerging AI concepts into production environments.
• Excellent communication skills and the ability to influence cross-functional teams through clear technical reasoning and vision.