Lead Forward Deployed Engineer (Agentic Systems)

We're hiring a Lead Forward Deployed Engineer to deploy autonomous AI agents into real enterprise workflows and to own the delivery outcomes of the team doing it. This is a player-coach role. You may join an established delivery team or build pods from scratch as clientele grows; either way, you own the program end to end, take it to production within 90 days, and keep every engagement moving. Hands-on work is a large part of the job, and not because there is no team. You are the backstop: when a seat is unfilled, a deliverable is off track, or a team member can't do the work, you do it yourself. Expect to spend much of your time on ground-level engineering (mapping messy customer processes, building and integrating agentic systems, owning production reliability) alongside hiring, managing, and reporting on the portfolio. We're looking for someone who has shipped LLM systems to enterprise production, has led a forward deployed or solutions engineering team, and is comfortable moving between the two in the same week. Position Overview We're hiring a Lead Forward Deployed Engineer to deploy autonomous AI agents into real enterprise workflows and to own the delivery outcomes of the team doing it. This is a player-coach role. You may join an established delivery team or build pods from scratch as clientele grows; either way, you own the program end to end, take it to production within 90 days, and keep every engagement moving. Hands-on work is a large part of the job, and not because there is no team. You are the backstop: when a seat is unfilled, a deliverable is off track, or a team member can't do the work, you do it yourself. Expect to spend much of your time on ground-level engineering (mapping messy customer processes, building and integrating agentic systems, owning production reliability) alongside hiring, managing, and reporting on the portfolio. We're looking for someone who has shipped LLM systems to enterprise production, has led a forward deployed or solutions engineering team, and is comfortable moving between the two in the same week. About Us Prentis is turning enterprises into AI-native companies. We're an AI research lab building computer-use models that see the screen and run software the way people do, including the ERPs and legacy tools that have no API. Our engineers don't hand off a model and walk away. They embed with client operators, rebuild the workflow, ship agents into production, and stay until the numbers hold. That work spans healthcare, logistics, trade, real estate, consumer products, and apparel, from adjudicating insurance claims to turning hours-long freight quotes into seconds. We're hiring engineers and forward deployed engineers who want to build alongside the people doing the work and see their code run on live volume, not in a demo. What Success Looks Like Week 3: discovery document delivered for the first client program (process map, integration inventory, success metrics, and an outcome baseline agreed with the customer) Day 60: pod standard published (pod composition, roles, rituals, artifacts, handoffs, and quality gates) Day 90: first program in production; two pod leads hired Every program: production within 90 days of kickoff, an agreed outcome baseline, and at least two reusable components contributed to the shared library Portfolio: blended gross margin above 50 percent across engagements Role & Responsibilities Hands-On Delivery Own one client program end to end: discovery → prototype → production rollout → iterate until measurable ROI Deliver the discovery document within three weeks of kickoff and agree the outcome baseline with the customer Design and build agentic systems with tool calling, planning/execution loops, state/memory, and human-in-the-loop (HITL) controls Implement reliability mechanisms: retries, timeouts, checkpointing, idempotency, and safe failure modes Build enterprise integrations (e.g., Slack/Jira/Salesforce/ServiceNow/SQL/SharePoint/knowledge bases), including auth, permissions, and auditability, and drive systems with no API (ERPs, legacy desktop tools) through computer-use agents Create evaluation and monitoring systems: offline regression suites, dataset replay, and production quality/cost/latency dashboards Debug real failures in production: non-determinism, flaky tools, prompt regressions, partial outages, and "agent got stuck" scenarios Step in on any program where delivery is at risk: take over the workstream, fix the failure, and hand it back Own the customer relationship at the program level: set expectations on scope and timelines, run checkpoint meetings, and manage change requests Contribute reusable components (connectors, eval harnesses, deployment templates) to the shared library from each program Team and Delivery Leadership Define and publish the pod standard, then hold every pod to it Hire, onboard, and manage pod leads and forward deployed engineers; review their designs against the reliability standard you set Own delivery outcomes across accounts: a single view of every engagement, a weekly status review, and every deployment to production within 90 days Manage engagement economics (scope, staffing, change control) to hold blended gross margin above target Unblock engineers, make scoping calls quickly, and escalation to the founding team when a decision needs them Report to the founding team on status, risk, margin, and resourcing; help shape product roadmap, technical strategy, and deployment playbooks Technical Problem Areas (Not Exhaustive) Long-running agent workflows with persistent state and memory across sessions Orchestration patterns for multi-step, multi-tool processes Computer-use agents operating GUI-only and legacy systems (ERPs, desktop tools) that expose no API; the mix of API-driven and screen-driven work varies by client Guardrails, verification, and human approval gates for high-stakes actions Reliability + observability (tracing, structured logs, prompt/tool versioning, replay) Enterprise security requirements: access control, audit logs, compliance constraints Evaluation and benchmarking of agent performance in real production environments Required Qualifications Five or more years of software engineering experience, at least two of them client-facing Shipped LLM systems to enterprise production: hands-on LLM application work including tool use, planning, state/memory, and reliability mechanisms Delivered at least two enterprise system integrations to production (e.g., CRM, ITSM, ERP, data warehouse, document or knowledge systems) Experience with evaluation of LLM/agent behavior (task suites, regression tests, acceptance metrics, quality thresholds) Production engineering mindset: tracing/logging, incident-style debugging, rollout/rollback strategies, versioning prompts/tools Strong software engineering fundamentals (Python and/or TypeScript), API integrations, and systems thinking Led a forward deployed, solutions engineering, or deployment team with direct reports Comfortable stepping in to do the work yourself when a seat is unfilled, a deliverable is off track, or the team can't do it Based in Los Angeles (preferred) or San Francisco, and able to be onsite in Los Angeles at least one to two times per month, two to three days per visit Preferred Qualifications Owned engagement P&L or margin targets for client delivery work Built or deployed computer-use or GUI automation agents against legacy enterprise software Forward deployed, deployment strategy, or solutions engineering experience at an AI platform company or a firm that deploys enterprise AI systems for clients Experience in the forward deployed or AI delivery practice of a large consultancy Time on the deployment team of a company shipping LLM-based agents for customer service, sales, or operations Experience deploying frontier-lab models through a partner or alumni program Experience growing a team from its first few engineers, including hiring loops and onboarding Experience with agent frameworks (e.g., LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI, DSPy) in real deployments Workflow engines for long-running processes (e.g., Temporal/Cadence, Airflow/Dagster as applicable) Built agent observability: OpenTelemetry-style traces, replay pipelines, prompt/version registries, cost/latency instrumentation Experience with enterprise software integration patterns and security/compliance constraints (PII, access control, auditability) Who You Are You are an engineer first: you would rather build the system than describe it, and you expect to build a large share of it yourself You are also a strategist: you embed with the client, define the business outcome, and build toward it You lead a team without losing the ability to do its work: when something breaks or someone can't deliver, you step in You think in systems: failure modes, tradeoffs, correctness, reliability scalability You're pragmatic and execution-oriented, you care about engineering quality, and you hold yourself and the team to it You make decisions with incomplete information and communicate them clearly You're direct about status, risk, and margin with engineers, enterprise stakeholders, and executives You're excited about the limits of agents and motivated to push them responsibly in production What We Offer Competitive compensation between $ 180 - 250k/year + meaningful equity, aligned with early-stage risk/reward Direct access to the founding team and high ownership from day one Authority to build the forward deployed function: the pod model, hiring, process, and playbooks are yours to define Resources to ship: strong engineering support and the compute/tools needed to deploy real agentic systems A high-velocity environment where you'll help define the product, the architecture, and the deployment playbook Location Los Angeles, CA (preferred) or San Francisco, CA. Onsite in Los Angeles at least one to two times per month, two to three days per visit, at minimum. #J-18808-Ljbffr