Full Stack AI Technology Specialist

Full Stack AI Tech Specialist

What You’ll Do

• Design and implement multi-agent AI systems using Google ADK, including agent orchestration, tool calling, memory, and reasoning workflows.
• Build end-to-end conversational AI experiences spanning frontend, backend services, data platforms, and AI/ML components.
• Develop reusable agent frameworks, APIs, and platform primitives that enable teams to rapidly build domain-specific AI agents.
• Integrate LLMs with enterprise data sources, ensuring secure access, policy enforcement, and governance-by-default.
• Implement human-in-the-loop patterns, evaluation loops, and guardrails for responsible AI behavior.
• Drive platform reliability, performance, observability, and cost efficiency for AI workloads.
• Act as a technical anchor for complex design decisions, proofs-of-concept, and production hardening.
• Partner with product, data science, and platform teams to translate AI use cases into scalable platform capabilities.

What You Bring

• Deep, hands-on experience building production-grade AI systems, preferably involving conversational AI, agents, or LLM-based architectures.
• Strong full-stack engineering expertise across backend services, APIs, data access layers, and modern frontend frameworks.
• Practical experience with multi-agent frameworks, orchestration patterns, or Google ADK (or equivalent agent platforms).
• Solid understanding of the AI/ML lifecycle: prompt engineering, tool use, evaluation, monitoring, and continuous improvement.
• Experience building cloud-native systems using containers, Kubernetes, CI/CD, and infrastructure-as-code.
• Strong grounding in enterprise data platforms, security, access controls, and privacy-aware system design.
• A clear point of view on responsible AI, trust, and human-in-the-loop augmentation as non-negotiable.

• Google ADK, LLM ecosystems, agent frameworks
• Python, Java, Springboot
• Modern frontend frameworks (React or equivalent)
• ML/AI tooling (LLMs, embeddings, vector stores, evaluation frameworks)
• Cloud platforms, Kubernetes, APIs, distributed data systems

Experience Level

Mid Level