Lead Software Engineer - Agent Safety

Help us use technology to make a big green dent in the universe! Kraken powers some of the most innovative global developments in energy. We create the technology that redefines utilities and unlocks a new energy system of the future. By optimising renewable generation, building a more intelligent grid, and empowering utilities to deliver an exceptional customer experience, our operating system is transforming the industry worldwide. It’s an incredibly exciting time to work in energy. Join us on our mission to improve the lives of ONE BILLION humans within the decade and shape a cleaner, better future for everyone. AI is a key investment area for Kraken Technologies as we look to expand our existing capabilities. A crucial part of this is broadening the foundational infrastructure to enable teams across the organisation to use AI effectively to accelerate our mission. You’ll work in the Agent Safety/Evals team, a new sub-team within AI Foundations. We build the shared platforms, harnesses, and guardrails that enable engineering and product teams to safely, reliably, and deterministically use machine learning and generative AI agents for internal systems and workflows across the business. This is a delivery-focused team that sits at the intersection of engineering and delivery, focusing on empowering our internal users. The Role We’re hiring a Lead Software Engineer to head our newly formed Agent Safety/Evals team. As AI agents take on more autonomous tasks across Kraken's internal workflows, this role is critical to ensuring they do so securely and predictably. This is a leadership role focused on constraining our internally facing AI agents to an expected operation space and guaranteeing system reliability. You will define the technical strategy for how we evaluate models, enforce safety guardrails, and govern AI behavior across internal platforms, skills, and harnesses. You’ll work closely with the broader AI Foundations team and engineers across Kraken to ensure that our push for rapid AI adoption in internal tooling never compromises on security, determinism, or quality. 🚀 What you'll own Lead the technical direction for internal agent safety: Design and implement systems focused on the reliability, security, and determinism of LLMs and autonomous agents, constraining them strictly to expected operational spaces within our internal ecosystems. Build robust evaluation frameworks: Develop scalable harnesses and evals tailored to internal workflows and skills. You will be responsible for asking the right types of questions about the quality and reproducibility of our evals, while engineering the systems to measure them robustly. Implement guardrails and governance: Create and enforce pre- and post-generation guardrails, managing the overarching governance of AI models operating within internal tools and platforms. Drive AI security and observability: Build out dedicated auth/permissions for internal AI agents, establish deep monitoring/observability pipelines, and define incident response protocols for AI-specific anomalies. Verification and Red Teaming: Lead continuous verification efforts and red teaming exercises to proactively identify vulnerabilities, prompt injections, or unpredictable behaviours in our internal AI implementations. Operate in AWS: Deploy, run, and support high-throughput, low-latency safety services for internal use cases; make sensible architecture/cost tradeoffs; partner effectively with platform/techops/security stakeholders. 🧠 What you bring to the party Strong technical leadership: Proven experience leading technical initiatives or teams, capable of setting the technical vision for complex, ambiguous domains. Deep software engineering fundamentals: Senior/advanced capability in designing secure components end-to-end, testing thoroughly, and reasoning heavily about system design, concurrency, and architecture tradeoffs. (Python preferred). Expertise in AI Eval