The posting
About the role
We’re looking for an AI Lead who combines outstanding software engineering judgment with a deep understanding of how software is built in the age of AI. This is a senior technical leadership role for someone who can set direction, build strong engineering capabilities, and fundamentally improve how a team designs, develops, tests, ships, and operates software by making effective use of AI models and agents.
You will lead the architecture and delivery of production-grade AI-enabled applications and agentic systems, while raising the engineering standards of the wider team. You will remain technically hands-on where it matters: shaping architecture, exploring difficult technical problems, reviewing critical changes, and helping engineers use AI effectively without compromising reliability, security, maintainability, or sound engineering practice. You will work closely with Product, Business, Data, and Engineering leaders to turn business priorities into valuable, measurable outcomes.
The central expectation is not simply to introduce AI tools into an existing development process, it is to rethink the engineering system itself: how work is decomposed, how context is provided to AI, how agents contribute across the software lifecycle, how humans review and govern their work, and how quality and delivery outcomes are measured. You should be able to explain concretely how you have used—or would use AI agents to increase the throughput and capability of an engineering team.
What you will lead and deliver
1. Build and manage an AI-native engineering team
- Define and continuously improve an AI-native software development approach across discovery, planning, design, implementation, testing, code review, release, and operations.
- Establish practical patterns for using coding agents to perform bounded engineering tasks, coordinate multi-step work, and contribute across the software lifecycle.
- Design workflows so engineers can delegate meaningful work to AI agents while retaining clear human ownership, review, and accountability.
- Create the foundations that allow the team to scale output through automation, reusable engineering capabilities, better context, and agent-enabled workflows
- Set expectations for effective use of AI tools: know when to delegate, when to intervene, how to validate generated work, and when not to use AI.
2. Provide technical and engineering leadership
- Set technical direction and make sound architecture and technology decisions across AI-enabled products, platforms, services, and integrations.
- Establish high standards for software design, modularity, API contracts, code quality, testing, security, performance, reliability, and maintainability.
- Lead by example through hands-on technical contribution, architecture reviews, design discussions, code reviews, and resolution of complex engineering challenges.
- Build an engineering culture based on ownership, strong fundamentals, continuous learning, pragmatic decision-making, and delivery of measurable outcomes.
3. Architect and deliver AI-powered systems
- Lead the design and delivery of production-grade applications powered by LLMs, AI agents/tools, and enterprise data and services.
- Develop customer-centric software, always evaluating every architectural choice based on how the user experiences it, as well as how the entire delivery chain experiences the software.
- Choose appropriate AI patterns for each problem, including deterministic workflows, model-driven reasoning, tool use, retrieval, memory, and multi-agent coordination, based on business value, complexity, reliability, and cost.
- Design agent workflows with clear responsibilities, boundaries, permissions, state management, failure handling, escalation, and human-in-the-loop controls where appropriate.
- Evaluate foundation models, platforms, and frameworks pragmatically; avoid unnecessary architectural complexity and framework-driven design.
4. Security Engineering & Threat Management
- Demonstrate a strong security-first engineering mindset, with a deep understanding of information security principles, threat modeling, and secure-by-design architecture.
- Identify, assess, and proactively address security threats across the entire software development lifecycle, including application architecture, AI agents, APIs, data access, infrastructure, and third-party integrations.
- Design systems based on realistic threat models, understanding attack vectors, trust boundaries, vulnerabilities, and potential impact rather than relying on superficial security controls or compliance checklists.
- Establish secure engineering practices covering identity and access management, authentication, authorization, least-privilege access, secrets management, encryption, data privacy, and secure API design.
- Understand and mitigate AI-specific security risks, including prompt injection, indirect prompt injection, data leakage, unauthorized agent actions, tool abuse, privilege escalation, and compromised agent workflows.
5. Ensure production quality and operational excellence
- Make systems that are monitored so well that the team discovers problems before the customer. Surprises don’t happen, and when they do, your monitoring explains what happened.
- Evaluate and test all AI & non-AI systems alike, at all levels. You fully understand how your products will behave in production and you can explain what happened when they break.
- Establish practices for tracing, monitoring, debugging, regression testing, model and prompt changes, deployment, rollback, and continuous improvement.
- Address security and risks through threat models and automated verification tools.
- Approach operational feedback systematically. We expect you to have anecdotes about how you’ve improved processes and software in response to feedback from customers or operational data.
6. Drive execution and business outcomes
- Translate business objectives into technical priorities, delivery plans, and measurable outcomes
- Partner with Product and business stakeholders to identify high-value opportunities where software, automation, or AI can create meaningful impact.
- Measure engineering effectiveness through outcomes such as cycle time, deployment frequency, quality, reliability, task completion, and the value delivered—not lines of code or tool adoption alone.
What we are looking for
- Strong software engineering fundamentals. You understand how to design, build, test, secure, deploy, and operate robust software systems. You can demonstrate sound judgment in architecture, data structures, distributed systems, debugging, code quality, and trade-offs.
- A track record of building and delivering real software products or platforms. We value depth of understanding and evidence of impact more than a fixed number of years in the industry.
- A clear, practical point of view on how software engineering changes when AI agents are part of the development team. Ability to decompose work, provide context, delegate tasks, coordinate agents, validate results, and preserve engineering accountability.
- Demonstrated ability to use AI coding tools and agents effectively in real engineering work. You understand their strengths and failure modes and know how to integrate them into disciplined development workflows.
- Evidence of increasing engineering productivity or delivery capacity through automation, tooling, process redesign, reusable systems, or agent-enabled execution. Experience helping a team scale its output without proportionally scaling headcount is a strong differentiator.
- Practical experience with LLMs and AI-powered applications, including model APIs, tool calling, structured outputs, agent workflows, and the evaluation of model-driven behavior.
- Ability to select the right architecture for the problem. You understand when to use conventional software, deterministic orchestration, retrieval, memory, or multi-agent approaches, and when simpler solutions are better.
- Strong proficiency in at least one modern backend or application engineering stack. Python is particularly relevant to our current AI work; experience with asynchronous systems, databases, cloud services, and distributed architectures is valuable.
- Understanding of production engineering practices, including automated testing, CI/CD, observability, performance, reliability, security, and cost management.
- Strong communication and collaboration skills, with the ability to work across Product, Business, Data, and Engineering and explain complex technical decisions in a clear, outcome-oriented way.
Useful experience
- Strong experience in software engineering, preferably 8–10 years, with demonstrated hands-on experience building and delivering AI-powered applications. We prioritize engineering depth, technical judgment, and the ability to leverage AI agents to transform software development over years of experience.
- Experience leading or contributing to an engineering team that has materially increased throughput through AI coding agents or agentic workflows.
- Experience building agentic applications or systems using frameworks such as LangGraph, Microsoft Agent Framework (MAF), or comparable technologies.
- Experience integrating AI agents with enterprise applications, APIs, data sources, and tools, including MCP servers and clients.
- Experience with model evaluation, agent tracing, and observability tools such as Langfuse, LangSmith, Arize Phoenix, OpenTelemetry, Grafana, or Datadog.
- Experience with retrieval, vector search, semantic search, knowledge graphs, or other data-grounding approaches where they solve a demonstrated product problem.
- Experience deploying and operating workloads on Azure, including relevant AI services, containers, and Kubernetes.
- Experience with responsible AI practices, AI governance, and relevant security or regulatory considerations.
Education
- A degree in Computer Science, Software Engineering, or a related field may be helpful, but it is not a substitute for demonstrated engineering capability.
- Equivalent practical experience and a strong record of delivery are welcome.
