Forward Deployed Engineer (PCS932)

Hire Overseas

4d•Remote

About The Position

The Forward Deployed Engineer will design and build full-stack AI applications, agentic systems, and production-grade AI infrastructure for enterprise clients. The role involves taking AI features from idea through production, working across React front ends, Python or Node services, RAG pipelines, and evaluations. It requires broad full-stack capability alongside deep specialization in at least one major AI platform ecosystem: Google, Anthropic, or OpenAI. The engineer will work directly with clients, communicate progress and technical trade-offs, and support junior engineers. The position is fully remote and full-time, with dedicated hours for team alignment and delivery. Availability during US business hours is required. Compensation is paid in USD twice monthly, on the 15th and 30th. Paid time off and holidays follow company policy and guidelines.

Requirements

3 to 5 years of software or data engineering experience, with extensive hands-on use of AI tools and LLM-based development over the past year.
Strong programming skills in Python and TypeScript or JavaScript, with experience building and consuming APIs.
Experience with front-end development in React or similar frameworks and at least one backend framework.
Hands-on experience with RAG, embeddings, and vector search, and with at least one agentic framework such as LangGraph, LangChain, Google ADK, Claude Agent SDK, or OpenAI Agents SDK.
Deep specialization in at least one major AI platform ecosystem: Google (Gemini, Vertex AI), Anthropic (Claude, MCP), or OpenAI (Responses API, AgentKit).
Working experience with at least one cloud platform (GCP, Azure, or AWS), and experience building and maintaining data pipelines.
Fluency with agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor.
Professional English proficiency at C1 or C2 level minimum.
Bachelor's or Master's degree in computer science, engineering, or a related field, or equivalent practical experience.
A certification on at least one major AI platform or cloud is required; candidates without one must obtain one within the first two months. Exam sponsorship and prep time are provided.
Submit an updated resume, a GitHub link or portfolio demonstrating production AI systems or full-stack projects, and a 1–2 minute Loom video introducing yourself and describing an end-to-end production AI system you owned, including your approach to evaluation and reliability. Both a portfolio and Loom video are required to advance.

Nice To Haves

Experience with MCP servers, multi-agent patterns, or LLM evaluation tooling such as LangSmith, Langfuse, or promptfoo.
Experience with Terraform or CI/CD pipelines.
Background in consulting, professional services, or a client-delivery environment.
A certification on at least one major AI platform or cloud is a strong differentiator at application.

Responsibilities

Develop user-facing interfaces in TypeScript and React, along with backend services and APIs in Python or Node.
Implement agentic behavior, including orchestration, tool and function calling, memory, and guardrails.
Build retrieval-augmented generation pipelines covering ingestion, chunking, embeddings, and vector and hybrid search.
Connect AI systems to enterprise data and applications through APIs, semantic layers, and protocols such as MCP.
Write evaluation suites and regression tests for LLM-powered features, monitoring cost, latency, and quality in production.
Apply engineering practices including version control, code review, automated testing, CI/CD, and observability.
Deploy on GCP, Azure, or AWS, and build and maintain the data pipelines that feed AI systems.
Use agentic coding tools such as Claude Code, Gemini CLI, Codex, or Cursor daily, applying judgment in verification and review.
Communicate progress, trade-offs, and blockers clearly to clients and project leads.
Support pre-sales when needed, including solution scoping, demo development, and effort estimation.
Mentor junior engineers and contribute to internal accelerators, reusable components, and engineering standards.