Lead AI Software Engineer, Technology & Digital, FT, 8:30A - 5P

Lead AI Software Engineer

The Lead AI Software Engineer will set the technical bar for design, build, and ship AI-native software. This is a hands-on, deeply technical role for an engineer who lives at the intersection of cloud platform architecture, generative and agentic AI, and AI-accelerated software delivery. This is a future-facing role. The toolchain, models, and frameworks named below will evolve — we are hiring for the judgment, depth, and adaptability to evolve with them and to lead others through that change.

What You'll Do

• Architect AI-native systems.
• Design secure, highly available, scalable applications and platforms on Google Cloud — from reference architecture through production.
• Own the hard decisions around availability targets, failure modes, data flow, latency, cost, and blast-radius containment.
• Drive spec-driven development.
• Establish and champion an AI spec-driven development framework (e.g., BMAD Method, Open Spec, Git Hub Spec Kit) as the team's delivery discipline — translating intent into executable specifications that humans and AI agents build against, review against, and verify against.
• Build with agentic AI.
• Design and deliver agentic systems: multi-agent orchestration, tool use, and retrieval — using the Agent2
  
  Agent (A2A) protocol for agent-to-agent interoperability and the Model Context Protocol (MCP) for tool and context integration, routed and governed through an agent / MCP gateway.
• Treat agents as first-class production software, with the same rigor for security, observability, and reliability as any other critical system.
• Move fast on research and POCs.
• Use AI coding agents (Claude Code, Codex, Antigravity, and successors) to compress the cycle from idea to working prototype to validated POC.
• Run structured experiments, evaluate models and approaches, and bring back evidence, not opinions.
• Engineer for the model layer.
• Apply Gemini and Vertex AI deeply - prompt and context engineering, grounding/RAG, tool calling, function/agent design, fine-tuning where warranted, and model selection trade-offs across quality, cost, and latency.
• Make it secure by design.
• Bake security and privacy controls into the architecture from day one, not as an afterthought, including agent identity and access management (verifiable, least-privilege, fully auditable identities and credentials for AI agents and other non-human workloads), data loss prevention, prompt-injection and jailbreak defenses, and content/safety filtering.
• Own quality and observability.
• Stand up evaluation harnesses, regression suites, and AI observability (quality/drift monitoring, tracing, Fin Ops/cost visibility) so that AI behavior is measurable, traceable, and accountable in production.
• Lead technically.
• Be part of core AI foundation team to set engineering standards, review designs and code (human- and AI-generated), mentor engineers on AI-native practices, and raise the team's collective ceiling.
• Partner with architecture, security, platform, and product stakeholders to land outcomes.

Estimated salary range for this position is $126148.63 - $163993.22 / year depending on experience.

Qualifications

• Degrees:
  Bachelors.
  
  Additional Qualifications:
  
  Bachelor's degree or higher in Computer Science or equivalent is required.
• 10+ years of professional software engineering experience, with a strong track record of shipping production systems (not just prototypes).
• Deep, hands-on Google Cloud (GCP) expertise — compute, networking, IAM, data, and the AI/ML stack.
• Able to architect a secure, high-availability system on GCP and defend the design.
• Domain depth in Gemini and Vertex AI — building real applications on the platform, including grounding/RAG, tool/function calling, and agent development.
• Extensive hands-on experience with AI coding agents — Claude Code, Codex, Antigravity, or equivalent — used for serious development, research, and rapid POC work (not casual autocomplete).
• You can speak to how you structure work for agents and where they help vs. hurt.
• Proven system-architecture ability — designing for security, high availability, scalability, fault tolerance, and cost-efficiency.
• Comfortable with distributed systems fundamentals and…