AI Engineer / FDE

Olliix(E&E Co.,Ltd); Fremont (CA); On-site; USD 130,000 - 180,000; Full time

Job summary

E&E Co., Ltd (DBA JLA HOME) in Fremont, CA seeks an internal forward deployed engineer to partner with business teams and deliver AI-enabled workflows. This on-site role spans discovery to deployment, with ownership of technical delivery across finance, sales, e-commerce and supply chain.

You will work with our China-based development team to build end-to-end applications, ensure reliability and security, and continuously improve solutions that impact daily operations.

Qualifications

• Bachelor's or Master's in CS/SE/Data Eng or related field or equivalent experience.
• Hands-on delivery of a business app/integration from requirements to post-release support.
• At least one working LLM-based workflow built for real users.
• Production reliability, security: access controls, retries, recovery and incident diagnosis.
• Ability to work through ambiguity and coordinate across US & China teams.
• Experience in inventory-based wholesale, retail or e-commerce is preferred; ERP/finance experience a plus.

Responsibilities

• Embed with the business to observe workflows and map automation opportunities.
• Define the problem, acceptance criteria, data sources and pilot scope.
• Design practical solutions and justify tradeoffs among tools, AI, security and cost.
• Build end-to-end internal applications, data pipelines and deployment with real data.
• Train users, document workflows and track benefits and backlog.

Skills

AI engineering

Reliability

Security awareness

Collaboration

Education

Bachelor/Master in CS/SE/Data Eng

Tools

LangGraph

Copilot Studio

n8n

MCP

Job description

E&E Co., Ltd (DBA JLA HOME) is preparing to modernize its enterprise systems and build AI-enabled operations to support long-term growth. Our operations span wholesale and retail, online and offline channels, B2B, B2C and D2C, and multiple marketplace storefronts. In this on-site hands-on role, you will work as an internal forward deployed engineer, partnering directly with business teams to understand their most important operational problems and turn them into working software and AI-assisted workflows. You will take selected solutions from discovery and prototype through deployment, user adoption and continuous improvement. Sitting within IT and partnering with Finance, Sales, Product, e-commerce, supply chain and our China-based development team, you will combine engineering depth with business judgment and own the technical delivery of solutions that improve how the company operates.

Responsibilities

Business Discovery and Solution Delivery

• Embed with the business. Work alongside the people who perform the work. Observe real transactions, decisions, handoffs and exceptions across Finance, Sales, Product, e-commerce and supply chain. Understand operational constraints and challenge unnecessary steps before deciding what to automate.
• Define the problem and expected result. Turn ambiguous requests into a clear business problem, a bounded workflow and testable requirements. Agree the process owner, data sources, baseline and acceptance criteria. Prioritize by business value, data readiness, effort and reuse, beginning with a limited first pilot.
• Design practical solutions. Make technical design and architecture decisions with IT. Choose appropriately among existing enterprise tools, conventional software, deterministic rules and AI. Explain the tradeoffs in reliability, user experience, security, implementation effort and operating cost to technical and non-technical stakeholders.
• Build applications end to end. Personally write maintainable code for internal applications, backend services, data pipelines and review interfaces. Build small working releases against real business data and iterate with users. Integrate solutions into the systems and steps where teams already work.
• Test and deploy into real operations. Validate representative normal and exception cases, reconcile relevant outputs to source records and run user-acceptance testing. Prepare monitoring, recovery, manual fallback and support ownership; deploy through approved release controls and resolve issues after launch.
• Own adoption and continuous improvement. Train users, document workflows and follow through on feedback and defects. Track eligible work completed through the solution, quality, cycle time and net manual effort including review and rework. Work with Finance and business owners to verify benefits and maintain a prioritized improvement backlog.

AI Workflows and Connected Systems

• Build AI into business workflows. Develop extraction, classification, retrieval, drafting, analysis support and exception-review workflows using approved LLM APIs and enterprise AI platforms. Initial use cases may include product information and listing preparation, sales and contribution reporting, customer follow-up preparation, or inventory and fulfillment exceptions.
• Evaluate AI behavior. Create evaluation cases from verified business examples, including difficult inputs, unsupported answers and unsafe tool actions. Validate structured outputs and source grounding; measure quality, latency and cost, and investigate failures. Use deterministic code for financial calculations and retain human approval for material financial and customer decisions.
• Connect systems and establish trusted data. Build approved interfaces across ERP/finance, order and inventory systems, WMS/3PL, retailer EDI, marketplaces, D2C and BI. Define identifiers, mappings, shared data definitions, lineage and reconciliation checks. Handle late, reversed and replayed records without omission or double counting.
• Contribute to enterprise modernization. Within approved projects, support legacy assessment, integration design, migration tooling, source-to-target validation, testing and cutover readiness with IT and implementation partners. Keep workflow components reusable as core systems evolve. The scope, specialist resources and authority for full system replacement follow the approved implementation plan.
• Operate securely and build for reuse. Apply version control, code review, automated testing, least-privilege access, secrets management, change control and incident response. Maintain versioned prompts, tool interfaces and evaluation suites. Package reusable connectors and services, document dependencies and costs, and enable IT, our China-based team and vendors to maintain delivered solutions.

Required Qualifications

• Education and experience. A bachelor's or master's degree in computer science, software engineering, information systems, data engineering or a related field, or equivalent professional experience. Typically three to eight years in software, data or business-systems engineering, including applications or integrations used in real operations.
• Demonstrated delivery. Hands-on experience taking a business application or integration from requirements through testing, deployment and post-release support. Explain your own code, design decisions, a production issue and the result achieved. Senior candidates must demonstrate ownership of an entire technical workstream; Engineer candidates may have delivered defined components under senior review.
• Applied AI engineering. At least one working LLM-based workflow you personally built for real users. Understand retrieval-augmented generation, tool use, structured outputs, evaluation and model limitations. Experience with an approved model API or enterprise AI platform; orchestration and automation tools such as MCP, LangGraph, Copilot Studio or n8n are useful, with no single stack required.
• Production reliability and security. Git, automated tests, code review, deployment, monitoring and incident diagnosis. Practical understanding of access controls, privacy, safe retries, duplicate-write prevention, recovery and manual fallback. Recognize prompt-injection and tool-permission risks, and protect business data throughout development and operation.
• Business judgment and ownership. Able to work through ambiguity, learn an unfamiliar process, identify the next useful action and explain technical tradeoffs clearly. Comfortable working directly with users and executives, revising an approach when evidence changes, coordinating across US and China teams and escalating dependencies before they jeopardize delivery.
• Relevant domain experience. Experience in inventory-based wholesale, retail, home furnishings or e-commerce is preferred, as is familiarity with order-to-cash, inventory, settlements and financial controls. ERP/finance implementation or migration, WMS/EDI, marketplace interfaces and BI experience are advantages. Working Mandarin is a plus; Palantir platform experience is not required.

Level and Growth Path

We are hiring at two levels and will set the level according to the candidate's experience and demonstrated results:

• AI Engineer. Typically three to five years of experience; has shipped business applications or integrations, built an AI workflow used by real users and can own defined components through testing, deployment and improvement under senior architecture and release review.
• Senior AI Engineer. Typically five to eight years of experience; has owned an operational software or AI workstream from business discovery through production deployment, adoption and measured results. Can independently lead technical delivery, make design decisions with IT, mentor engineers and coordinate implementation partners.

Growth path: AI Engineer Senior AI Engineer Business Systems & Automation Lead or Data & AI Engineering Lead Director, Business Systems & AI. The company funds platform certification (for example NetSuite, Microsoft Dynamics 365, SAP, Azure or AWS) and supports part-time graduate study for strong performers.