We are building a portfolio of agentic operations — production-grade AI agents that plan, act, and continuously improve the everyday experiences of our employees. The goal is simple and ambitious: eliminate friction from the moments that matter so employees spend more time on work that matters and less time on tickets, forms, and queues.
As Executive Director, Software Engineer, you will own the end-to-end technical direction and hands-on architecture of the Digital Employee Experience and the flagship agents built on it. You will lead a multi-disciplinary engineering group , partner with product, IT, cyber, and controls, and be personally accountable for the reliability, safety, cost, and business outcomes of every agent that reaches employees.
This is a hands-on senior engineering leadership role: you will write code, design systems, run production, and set the technical bar — while coaching a team of engineers across squads.
Job responsibilities
- Own the reference architecture for agentic operations: orchestration, planning, tool-use, memory, retrieval (RAG / GraphRAG), evaluation, guardrails, observability, human-in-the-loop, cost governance, and lifecycle (build → evaluate → deploy → monitor → retire).
- Choose and evolve the stack (agent frameworks, model gateway, vector stores, feature/knowledge stores, workflow/queue, telemetry, red-team & eval harness). Balance make-vs-buy against firm standards and controls.
- Define the golden path so any employee-experience squad can ship a safe, evaluated agent in weeks — not quarters.
Flagship employee-experience agents
- Ship agents that measurably deflect / auto-resolve high-volume employee journeys, e.g. IT service desk incidents, access & entitlements.
- Design multi-agent and human-in-the-loop patterns that know when to act, when to draft, and when to escalate.
- Instrument every agent with business KPIs (deflection rate, time-to-resolve, CSAT, cost per interaction) and quality KPIs .
Reliability, safety, and controls
- Establish SRE for agents: SLOs, error budgets, canaries, circuit breakers, prompt/model version pinning, deterministic replay, rollback.
- Partner with cybersecurity, privacy, model risk, controls, and audit to make responsible AI concrete: data minimization, purpose limitation, PII handling, evaluation before change, model & prompt inventory, drift detection, red-teaming, jailbreak defense, secret and tool-use hygiene.
- Own the evaluation harness: offline benchmarks, online A/B, gold sets, LLM-as-judge with human calibration, regression gates in CI/CD.
Delivery leadership
- Lead squads via principal/lead engineers; set the technical bar via architecture reviews, code quality standards, and design docs.
- Grow senior Italent (staff / principal); run a strong hiring bar; sponsor diverse talent.
Business partnership
- Communicate crisply to Managing Directors and executive stakeholders — trade-offs, risks, timelines, and results.
Required qualifications, capabilities and skills
- 12+ years of software engineering experience, including 5+ years leading engineers and 3+ years shipping production ML/AI (with at least 1+ year shipping LLM- or agent-based systems to real users).
- Deep hands-on expertise in at least one modern language (Python, TypeScript, Java, Go) and comfort operating polyglot codebases.
- Track record of designing and running large-scale distributed systems in production (SLOs, on-call, incident command, cost).
- Practical experience with the agentic stack: LLM orchestration (e.g. LangGraph, LlamaIndex, Semantic Kernel, custom), tool/function calling, RAG, evaluation harnesses, guardrails, prompt & model versioning, model gateways. Hands-on with Model Context Protocol (MCP) architectures — designing servers/clients, tool schemas, capability negotiation, and secure enterprise deployment patterns.
- Strong grounding in data platforms (Databricks / Snowflake / lakehouse patterns), event streaming, and modern MLOps/LLMOps (CI/CD, feature/prompt/model registry, observability).
- Proven leadership in responsible AI, security, and controls for enterprise deployments: PII/PCI/PHI handling, model risk, red-teaming, jailbreak & prompt-injection defense, secret & tool-use hygiene.
- Executive-level communication: you can move fluently between a whiteboard architecture, a code review, and a conversation with a Managing Director.
- Bachelor's in Computer Science or related discipline, or equivalent industry experience.
Preferred qualifications, capabilities and skills
- Experience building employee-facing products at scale (IT service desk, HR tech, knowledge management, workplace assistants).
- Experience integrating with different data sources, ServiceNow, identity providers, endpoint management, and enterprise search.
- Experience with multi-agent patterns (planner-executor, supervisor-worker, debate/critique) and long-horizon workflows.
- Contributions to open source, published research, patents, or public talks in AI/agents/SRE.
- Prior experience in a highly regulated environment (financial services, healthcare, public sector) with model risk management (SR 11-7 or equivalent).
- Master's or PhD in a quantitative discipline.