We're forming small AI-native teams where AI coding agents are the default way to build software, whether that's web apps, APIs, data pipelines, modernization or agentic systems for clients. This role is about engineering with agents, not only building them. As Lead, you define how the team works with agents, own delivery quality, and work directly with client stakeholders. Responsibilities Lead technical delivery of a small AI-native team, from problem framing and specs to production Design and evolve the team's agentic SDLC: spec-driven workflow, agent configuration, parallel and background agent work, review and quality gates Set up repos for agents: instructions, prompt files, skills, custom agents, MCP servers, plus the architecture, docs and tests that let agents work reliably Define how AI-generated code is checked: TDD, test automation, code review, static and security analysis in CI/CD Set safe-use rules for agents: permissions, secrets, client data, approved tools Measure and optimize AI cost and delivery impact (tokens and premium requests, model choice, cycle time, rework) Own the solution architecture; mentor engineers in agentic practices; contribute to PoCs, estimates and presales Requirements 7+ years in software engineering, with hands-on Java, .NET or Python, and strong design and debugging skills 6+ months of daily, hands-on use of AI coding agents (GitHub Copilot agent mode / CLI / coding agent, Claude Code or Codex) on real projects Deep context engineering for coding agents: repo instructions, prompt files, skills, custom agents and subagents, MCP Spec-driven development in practice, including breaking work into tasks an agent can do well Proven practices for keeping AI-generated code at production quality: TDD, test automation, reviewing large AI diffs, CI/CD quality gates Understanding of agent security risks: permissions, secrets, data leakage, prompt injection through repos and tools Practical AI cost awareness: model selection per task, token and premium-request economics, measuring productivity impact Solution architecture skills; experience leading and mentoring engineers and working with clients English level B2+ Nice to have Experience building LLM or agentic applications (agent frameworks, RAG, tool calling, evals, guardrails, LLM observability) Showcase of rolling out AI-assisted development to other teams (enablement, playbooks, metrics) Cloud and AI certifications (Azure, AWS, GCP, GitHub Copilot, Anthropic) Business analysis mindset with consulting or presales experience Familiarity with UX/UI and the financial services domain
