AI Engineer

• Solution Architecture & Agentic Development: Build and optimize production-grade applications using advanced Gemini and Claude patterns, ensuring scalable code design, thorough technical documentation, and measurable business outcomes.
• ADK Multi-Agent Engineering: Core development of autonomous multi-agent systems, managing complex agent orchestration, deterministic state workflows, human-in-the-loop approval gates, and resumable state architectures.
• AI Evaluation & Security (EvalOps): Drive system trust and reliability by establishing rigorous model-quality testing, adversarial prompt testing, automated prompt inspection, and responsible AI guardrails.
• Google Cloud Production Security: Implement enterprise-grade security controls around AI workloads using Vertex AI, Gemini Enterprise, Model Armor, IAM policies, and VPC Service Controls (VPC-SC).
• Full-Stack Integration: Independently develop end-to-end features, delivering secure backend APIs, application user interfaces, data integrations, and the supporting infrastructure required to run AI models.
• Production Platform Engineering: Package and release AI capabilities into production using standard CI/CD pipelines, release controls, quick rollback capabilities, centralized logging, system monitoring, and cost-efficiency optimization.

• Google Cloud AI Ecosystem: Advanced hands-on experience with Vertex AI, Gemini Enterprise, Model Armor, and Google Cloud platform security architectures (IAM, VPC-SC).
• Agentic Engineering & LLMs: Proven experience working with multi-agent frameworks, prompt engineering, fine-tuning, model selection, and deterministic application workflows.
• Full-Stack Software Engineering: Strong programming foundations with the ability to independently write, test, and maintain production code for UI, APIs, and cloud architecture.
• Platform & MLOps: Experience managing the release lifecycle of AI applications, tracking compute costs, and implementing automated testing frameworks.

Personal Requirements

• Proactive Ownership: A self-driven approach to technical ownership, with a focus on independently managing features from initial concept through to live production deployment.
• Pragmatic Problem-Solving: Strong engineering mindset that balances advanced AI research with practical, dependable, and cost-effective product solutions.
• Documentation & Knowledge Transfer: Exceptional communication skills with a focus on writing clear developer documentation and executing structured knowledge handoffs to technical teammates.