Forward Deployed Engineer

Leena AI

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Location: United States
Job Type: full-time
Work Format: 🌍 Remote
Salary: Not specified
Posted: September 27, 2026

Job Description

As a Forward Deployed Engineer at Leena AI, you will embed directly with our most strategic enterprise customers to drive transformational AI adoption in HR, IT, and Operations. You will collaborate closely with customer teams to ship advanced AI applications—AI Colleagues, workflow automations, and enterprise integrations—that solve real-world business problems. This is not just a spec-writing role. You are in the field, inside the client’s environment, building and deploying production systems that move organizations from manual processes to AI-powered automation at scale.

The Forward Deployed Engineer owns technical delivery end-to-end of our most challenging customers. You'll gather requirements directly from the client, design the integration blueprint, align stakeholders on the approach, and then build it—writing production code, configuring the platform, and connecting enterprise systems. Beyond go-live, you continue to own the deployment: troubleshooting issues, evolving the implementation as client needs change, and serving as the technical point of contact through the lifetime of the account. Working closely with our Post-Sales, Product, and Engineering teams, you'll partner with engineers to unblock complex problems and feed real-world learnings back into the product. As one of our early FDEs, you'll help shape Leena AI's go-to-market motion. We expect our FDEs to operate autonomously, thrive under ambiguity, and represent Leena AI at the highest level in customer environments.

What We’re Looking For
Our customer conversations are 90% technical. We need one person who brings three things together—and that person is rare.

Platform Mastery. You go deep on the Leena AI platform—fast. Within weeks, you can configure it, build on top of it, and push it to its limits inside a customer's environment without waiting for someone to tell you what's possible.
Integration Expertise. Integrations are the highest-risk element of every enterprise deployment. You own and mitigate this risk. You navigate customer APIs, access constraints, and messy enterprise environments and get integrations live anyway.
Customer Ownership. You control the room. You operate consultatively, adapt without a playbook, and build the trust that makes customers believe Leena AI is the right partner. You don’t need a PM to run point—you are the point.

Key Responsibilities
Integration Build & Execution

Design integration blueprints and solution architectures directly with the client, then own the hands-on execution—translating your designs into working systems without losing fidelity to agreed scope.
Prototype custom integrations, API connectors, and workflow automation in Python, TypeScript, and JavaScript across enterprise systems including Workday, ServiceNow, SAP, Oracle, and UKG.
Build and configure AI Colleagues, Agent Operating Procedures (AOPs), and Skills directly inside the client's environment—working within their systems, security constraints, and timelines.
Navigate real-world enterprise constraints—SSO/SAML/OIDC, legacy HRIS, data residency, and IT security gatekeeping—to get integrations across the finish line.
Author Solution Design Documents (SDDs) with precision—flagging scope gaps or technical blockers early and looping in PMs or Engineering before they hit the client timeline.
Identify non-standard requirements early and align internal engineering resources to close gaps before they block delivery.
Own the 2-week hypercare period post go-live, ensuring stability and rapid resolution of any production issues.

Enterprise Integration & Platform Expertise

Understand and configure end-to-end integrations with HRIS, ITSM, payroll, and directory systems using REST APIs, webhooks, and out-of-box Leena AI connectors.

Understand and configure complex authentication flows including OAuth 2.0, SAML, and API key management across client enterprise environments.
Build and validate test cases for both internal UAT and client UAT, ensuring quality before go-live.
Debug and resolve 400/500-series API errors, integration failures, and platform configuration issues with urgency and precision.

Client Partnership & Escalation Management

Serve as the senior technical point of contact for enterprise clients during active implementations, participating in steering committees and technical working sessions.
Act as a technical escalation resource for the broader Professional Services team—triaging issues, unblocking SCs and PMs, and providing authoritative answers to client technical questions.
Proactively communicate risks, dependencies, and blockers to PMs and leadership before they become CIO-level escalations.
Participate in pre-call readiness reviews to ensure no client-facing question goes unanswered.

Knowledge Sharing & Team Enablement

Identify and codify repeatable integration patterns from each deployment—building a library of reusable templates, connector configurations, and self-serve documentation that accelerates future FDE engagements.
Contribute to the AIC self-serve lifecycle documentation library and platform backend training materials.
Provide feedback to Solutions Consultants on what is and is not buildable within the platform—helping sharpen the quality of integration blueprints handed to the FDE team on future engagements.
Contribute at least 3 reusable knowledge assets per quarter to the PS team knowledge base.

Requirements

Requirements

8+ years in a customer-facing engineering, professional services, or forward deployed role.
Proven track record of deploying production-grade software inside enterprise client environments, not just in sandboxes.
Strong proficiency in Python and comfort writing production-quality code under real-world time constraints.
Deep experience with REST/SOAP APIs, OAuth 2.0, SAML/SSO, and enterprise integration patterns.
Hands-on experience with enterprise HR or IT systems: Workday, ServiceNow, SAP, Oracle PeopleSoft, UKG, SuccessFactors, or similar.
Ability to navigate ambiguous, fast-moving situations and deliver outcomes without perfect requirements.
Executive presence and confidence leading technical discussions with CIOs, IT Security teams, and senior enterprise stakeholders.
Experience in a fast-paced startup or high-growth SaaS environment where you built structure from ambiguity.
Maintain current knowledge of enterprise AI deployment patterns, LLM capabilities, agentic frameworks, and integration tooling—bringing emerging techniques back into Leena AI’s delivery playbook.

Preferred Qualifications

Experience deploying AI/LLM-based products, agentic orchestration frameworks (LangGraph, CrewAI), or RAG-based knowledge systems.
Background in HR tech, ITSM optimization, or enterprise automation consulting.
Familiarity with Leena AI’s platform including AIC, AOPs, Skills, and the Flow Dashboard.
Experience building or contributing to implementation playbooks and reusable delivery frameworks.
Track record of mentoring junior engineers or SCs on technical concepts.

Benefits

Why Join Leena AI?

Work at the forefront of enterprise AI and automation with a company that is defining the trends in this space—not following.
Be the engineer who actually builds the integrations that make AI real inside the world’s largest companies—not the person who demos it or draws the diagram.
Partner with a global team of innovators building industry-defining technology across HR, IT, and operations.
Be part of a dynamic, high-growth company where your work directly impacts client retention, expansion, and Leena AI’s reputation in the market.
Competitive compensation, meaningful equity, performance bonus tied to deployment outcomes, and rapid career growth opportunities.

🎯 Who is this job for?

Senior-level candidates with 8+ years of customer-facing engineering, professional services, or forward deployment experience are best suited for this role.

They should be highly skilled in Python, REST/SOAP APIs, OAuth 2.0, SAML/SSO, enterprise integrations, and systems such as Workday, ServiceNow, SAP, Oracle, or UKG, with additional familiarity with AI/LLM and agentic technologies.

They should be comfortable designing and deploying production integrations, configuring AI workflows, troubleshooting enterprise systems, leading technical discussions with senior stakeholders, managing go-lives and hypercare, and working autonomously in ambiguous environments.

💬 Potential Interview Questions

How would you design an integration between Leena AI and an enterprise HRIS such as Workday or UKG?

I would begin by defining business workflows, data ownership, API capabilities, security requirements, and error-handling expectations. Then I would document the architecture, authentication flow, data mappings, webhook or polling strategy, retry behavior, monitoring, and UAT plan in a Solution Design Document.

How would you implement OAuth 2.0 authentication for a customer API integration?

I would select the appropriate grant type, securely store client credentials and tokens, implement token acquisition and refresh logic, and restrict scopes to the minimum required permissions. I would also validate expiration, revocation, error responses, and secret rotation procedures.

What is the difference between SAML and OIDC, and when would you use each for enterprise SSO?

SAML is an XML-based protocol commonly used for browser-based enterprise authentication, while OIDC builds on OAuth 2.0 and uses JSON and JWTs. I would use the protocol supported by the customer’s identity provider and validate claims, audience, issuer, signing certificates, and logout behavior.

How would you troubleshoot a production integration returning intermittent 400 and 500 errors?

I would correlate request IDs and timestamps across application logs, inspect request payloads and response bodies, and determine whether the issue is caused by validation, authentication, rate limiting, or the downstream service. I would reproduce the failure safely, add appropriate retries for transient errors, and communicate impact and mitigation to stakeholders.

How would you build a reliable REST API connector in Python?

I would separate authentication, transport, business logic, and data-mapping layers, using timeouts, structured logging, schema validation, retries with backoff, and clear exception handling. The connector would include unit tests, integration tests, idempotency controls, and configuration managed outside the source code.

How would you design an AI Colleague or agent workflow for an HR or IT use case?

I would define the user intent, permitted actions, required enterprise data, escalation paths, and safeguards before configuring the agent. The workflow should use grounded knowledge, explicit tool permissions, validation before write operations, auditability, and human handoff for ambiguous or high-risk requests.

What techniques would you use to reduce hallucinations in a retrieval-augmented generation system?

I would use high-quality document ingestion, chunking and metadata strategies, permission-aware retrieval, reranking, and prompts that require answers to be grounded in retrieved sources. I would also evaluate retrieval accuracy, citation quality, refusal behavior, and production feedback using representative test cases.

How would you handle a customer requirement that is not supported by the platform?

I would clarify the business outcome, distinguish essential requirements from preferences, and assess whether configuration, a custom integration, or a product change can meet it. I would document the gap, risks, timeline, and alternatives, then align with Product and Engineering before making a customer commitment.

How would you plan UAT and the two-week hypercare period for an enterprise deployment?

I would create test cases covering normal flows, permissions, edge cases, failures, security, and data reconciliation, with clear acceptance criteria and ownership. During hypercare, I would monitor integrations and user feedback, triage incidents by severity, maintain a recovery plan, and provide regular status updates until the system is stable.

How would you secure and operate an enterprise AI integration across multiple customer environments?

I would enforce least-privilege access, tenant isolation, encryption, secure secret management, audit logging, data retention controls, and customer-specific residency requirements. Operationally, I would establish monitoring, alerting, deployment controls, incident procedures, and reusable implementation documentation without exposing tenant data.

📋 Job Summary

Leena AI is hiring a remote, full-time Forward Deployed Engineer in the United States to lead enterprise AI deployments across HR, IT, and Operations. You’ll design and build production integrations, AI Colleagues, workflow automations, and enterprise connectors while partnering directly with customers and owning delivery through go-live and hypercare. The stack includes Python, TypeScript, JavaScript, REST/SOAP APIs, OAuth 2.0, SAML/SSO, Workday, ServiceNow, SAP, Oracle, UKG, and emerging LLM and agentic frameworks. Salary is not disclosed, but the role offers competitive compensation, equity, performance bonuses, rapid growth, and the chance to shape how enterprise AI is deployed at scale.