Forward Deployed Engineer

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SeniorOn-siteFull-time

#458984·Dodano miesiąc temu·3

Źródło: LinkGroup

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Tech Stack / Keywords

Python TypeScript JavaScript Go Generative AIModel Context ProtocolRAGprompt engineering LLMAI coding assistants

Podsumowanie oferty

The Forward Deployed Engineer will architect and deploy advanced Generative AI systems including multi-agent orchestration and autonomous agents. They will write production-grade code primarily in Python, TypeScript/JavaScript, and Go. Responsibilities include integrating Model Context Protocol (MCP) servers, rapid prototyping to production, and implementing performance evaluation frameworks. The role requires leveraging AI coding assistants as a main development tool and focuses on delivering high-velocity AI-first workflows.

Wymagania

6+ years of experience in full-stack or AI/ML software engineering with a strong track record of shipping production software.
Deep familiarity with Generative AI patterns including agents, multi-agent frameworks, RAG, prompt engineering, and LLM evaluation.
Direct experience with Model Context Protocol (MCP) servers, headless/background agents, and harness engineering.
Strong proficiency in Python and solid capability in TypeScript/JavaScript, Go, or equivalent.
Experience in startup, R&D lab, or fast-paced innovation environments.
Mastery of AI coding assistants (e.g., Claude Code, GitHub Copilot, Databricks AI Assistant, Cursor) as primary workflow.

Nice to have:

Experience in regulated industries such as Pharmaceuticals, Healthcare, or Financial Services.
Familiarity with compliance standards and governance frameworks like GxP, GDPR, or SOX.

Obowiązki

Architect, build, and deploy sophisticated GenAI solutions including multi-agent orchestration systems, autonomous headless agents, harness engineering, and advanced RAG architectures.
Write clean, production-grade code using Python, TypeScript/JavaScript, Go, or similar languages.
Build, extend, and maintain MCP servers and agent toolings to connect AI agents with external systems, APIs, and data stores.
Turn rough sketches and concepts into robust software rapidly while maintaining quality.
Use AI coding assistants as primary development interfaces to accelerate delivery.
Implement evaluation frameworks to benchmark agent performance, accuracy, hallucination rates, and latency in production.

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