AI Developer
fluttergroup
Job Description
- Enterprise AI Platform Development: Design, build, and evolve Flutter's enterprise AI assistant and multi-agent platforms, creating intelligent systems that integrate seamlessly with internal applications, data sources, and workflows across the organisation.
- LLM Inference & Integration: Work hands-on with LLM inference systems — including LiteLLM and AWS Bedrock — to build responsive, scalable, and cost-efficient AI applications, navigating model selection, routing, and deployment trade-offs.
- Context Engineering & Prompt Architecture: Go beyond basic prompt engineering to architect sophisticated context pipelines — designing retrieval strategies, memory systems, and context windows that maximise model performance within real-world constraints.
- Model Tokenomics & Performance Trade-offs: Apply a deep understanding of token economics — balancing latency, cost, context length, and model capability to make informed architectural decisions across production AI systems.
- Guardrails & Safety Systems: Implement robust model constraint and guardrail frameworks to ensure AI outputs are safe, compliant, and aligned with Flutter's Responsible AI Policy and regulatory obligations across 20+ markets.
- Agentic AI Development: Build and deploy sophisticated agent architectures using frameworks such as LangChain, LangGraph, Strands, ADK (Agent Development Kit) , and similar tools — creating multi-step reasoning systems, tool-using agents, and orchestration layers that solve complex enterprise problems.
- Full-Stack AI Solutions: Deliver end-to-end AI applications, owning the problem from model integration through backend services to user-facing interfaces — demonstrating the ability to work across the full stack when the problem demands it.
- API & Integration Development: Design and implement integrations using AWS services (Bedrock, Lambda, API Gateway) and modern protocols including Model Context Protocol (MCP) and REST/streaming APIs.
- Security-First Development: Bring a security-oriented mindset to all AI development — considering prompt injection risks, data leakage vectors, authentication flows (OAuth, JWT), access control, and safe handling of sensitive enterprise data from the ground up.
- AI-Augmented Engineering Practices: Embrace and champion agentic development practices, using tools such as Claude Code, OpenCode, GitHub Copilot , and similar AI coding assistants to accelerate delivery, improve code quality, and explore new approaches to software development.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and engineering teams across Flutter's global organisation to deliver AI solutions that drive measurable business impact.