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 escape.
- 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.

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📌 Senior Director Software Engineer, AI Engineering -DEX Products (Buenos Aires)
🏢 JPMorgan Chase
📍 Buenos Aires