Forward Deployed Engineer (FDE)

Forward Deployed Engineer (FDE) – Placeholderworks

About Placeholderworks

Placeholderworks is an AI engineering firm focused on turning AI concepts into robust, production-grade systems. We design, build, deploy, and support AI-powered applications, intelligent agents, and retrieval systems that integrate with existing business workflows. Our team works closely with clients to solve real-world problems through practical, reliable, and scalable engineering solutions.

About the Role

We are looking for a Forward Deployed Engineer (FDE) with 2–4 years of experience who can bridge the gap between business problems and technical execution. You will work directly with clients to understand their challenges, identify opportunities for AI and automation, design technical solutions, and build and deploy production-ready systems.

This is a hands-on engineering role that combines client discovery, software development, AI engineering, and system integration. You will own projects from initial discussions through implementation, deployment, and continuous improvement.

Key Responsibilities

• Work directly with clients to understand business requirements, workflows, and technical challenges.
• Translate business problems into practical technical solutions and implementation plans.
• Build and deploy AI-powered applications, LLM workflows, intelligent agents, and RAG systems.
• Develop backend services, REST APIs, and integrations with third-party platforms and existing business systems.
• Connect AI applications with databases, CRMs, internal tools, and document repositories.
• Build rapid prototypes and validate solutions through client feedback.
• Deploy, test, debug, and maintain applications in production environments.
• Improve AI reliability through evaluation, structured outputs, guardrails, and error handling.
• Optimize application performance, latency, scalability, and operating costs.
• Communicate technical decisions, explain trade-offs, and present solutions to clients and stakeholders.
• Take end-to-end ownership of projects from discovery to production deployment.

Required Qualifications

• 2–4 years of professional experience in software engineering, AI engineering, or a related technical role.
• Strong proficiency in Python and/or TypeScript/JavaScript.
• Experience building backend services, REST APIs, and integrating third-party systems.
• Hands-on experience with LLM APIs such as OpenAI, Anthropic Claude, or Google Gemini.
• Understanding of prompt engineering, structured outputs, tool calling, and AI agent workflows.
• Familiarity with RAG, embeddings, vector databases, and document-processing pipelines.
• Working knowledge of SQL databases, Git, debugging, and software testing.
• Experience with Docker and at least one cloud platform, such as AWS, GCP, or Azure.
• Ability to troubleshoot technical issues and deliver reliable, maintainable software.
• Strong communication and problem-solving skills.
• Ability to work directly with clients, clarify requirements, and translate feedback into technical improvements.
• Demonstrated ownership of projects from requirements gathering through deployment.

Preferred Qualifications

• Experience deploying AI-powered applications or LLM workflows in production.
• Familiarity with LangGraph, LangChain, LlamaIndex, or similar frameworks.
• Experience integrating enterprise platforms, CRMs, or legacy systems.
• Familiarity with CI/CD, logging, monitoring, and production troubleshooting.
• Understanding of AI evaluation, hallucination mitigation, security, and cost optimization.
• Experience with React or Next.js.
• Previous experience in a startup, consulting, or client-facing engineering role.

What We Value

• Ownership: Take responsibility for outcomes, not just assigned tasks.
• Execution: Turn ideas into working software that solves real problems.
• Adaptability: Learn new technologies and work through ambiguity.
• Customer Focus: Understand the business problem before choosing a technical solution.
• Engineering Excellence: Build reliable, maintainable, production-ready systems.

What Success Looks Like

• Turning unclear business requirements into effective technical solutions.
• Moving from prototypes to production-ready applications.
• Integrating AI systems with real-world business data and existing software.
• Resolving deployment challenges and improving system reliability.
• Building strong client relationships through clear communication and dependable execution.
• Delivering measurable business value through AI and automation.

If you enjoy solving real-world problems, working directly with clients, and taking AI-powered solutions from concept to production, we would love to hear from you. At Placeholderworks, the goal is not just to build AI. It is to make AI work in the real world.