Lead Software Engineer - Agent Safety
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Kraken is the operating system for the energy transition. We help energy companies, utilities, and system operators transform how they operate so the world can move faster towards a zero-carbon future. We build technology that solves real, messy problems at scale - across data, software, and increasingly AI. Our teams move fast, take ownership, and care deeply about impact.
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.
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.
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 Evaluation and Safety: A highly critical thinker who understands the nuances of LLM behavior. You must be comfortable interrogating the quality of AI outputs and deeply experienced in building harnesses to measure it reproducibly.
Security and Governance mindset: Experience with threat modeling, authentication, red teaming, or building guardrails for internal systems, tooling, or platforms.
Cloud experience (AWS): Comfortable running internal services, owning reliability/scalability, and collaborating with platform/techops/security partners.
Excellent communication: Able to synthesize complex safety and eval metrics into actionable insights for both technical and non-technical stakeholders.
Confidence in Internal Tooling: Engineers across Kraken can deploy new autonomous models, AI skills, and internal agents with total confidence, knowing they are bound by reliable safety guardrails and strict operational constraints within our internal ecosystems.
High-Quality Internal Evals: You have established a culture and infrastructure of robust, highly reproducible evaluation metrics that accurately reflect the real-world performance and safety of our internal-facing AI tooling and harnesses.
Resilient Internal Infrastructure: AI security, monitoring, and incident response are treated as first-class citizens, ensuring that any erratic agent behavior in our internal platforms is immediately caught and mitigated before it affects broader operations.
Technical Leadership: Strong collaboration across AI Foundations helps accelerate secure internal AI adoption; you are actively mentoring engineers and shaping the company's internal AI governance strategy.
Experience with specific LLM evaluation and safety frameworks (e.g., Inspect AI, Ragas, OpenAI Evals, NeMo Guardrails).
Django experience and strong backend engineering patterns (security, performance, maintainability).
Experience with Datadog for complex observability, tracing, and monitoring in AI environments.
Familiarity with foundational AI engineering tooling like Pydantic AI, LiteLLM, or LangChain.