Forward Deployed Engineer, GenAI, YouTube

Minimum qualifications:

• Bachelor's degree in Computer Science, Electrical Engineering, Math or a related quantitative field, or equivalent practical experience in software development.
• 4 years of experience in full-stack software development and system design.
• Experience with back-end languages (e.g., Java, Python, or C++).
• Experience with front-end languages (e.g., JavaScript or TypeScript).
• Experience working with database technologies (e.g., SQL, NoSQL).
• Experience with Generative AI (e.g., Large Language Models (LLMs), diffusion models), development workflows, and use cases.

Preferred qualifications:

• 4 years of experience with cloud or microservice architectures and building multi-step Large Language Model (LLM) applications.
• 4 years of experience designing databases, vector databases, embedding generation, and resolving data-readiness challenges for AI systems.
• 2 years of experience with Site Reliability Engineering or DevOps practices and building LLM evaluation pipelines.
• 2 years of experience collaborating with stakeholders on project requirements and Go-To-Market operations or CRM integrations.
• Experience in technology consulting or entrepreneurial environments, delivering zero-to-one product initiatives and building scalable products.
• Experience in a technical consultant, technical pre-business engineer, enterprise architect, systems engineer, or customer-facing role.

About the job

Responsibilities

• Serve as the engineering point of contact for delivering complex AI applications, transitioning from rapid prototypes to production-grade, AI-powered solutions (e.g., multi-agent systems, MCP servers) that drive measurable return on investment.
• Execute the full software development lifecycle, authoring technical design documents and project plans, writing, testing, and shipping clean, maintainable code, building intuitive frontend surfaces, and executing delivery from initial scoping through to deployment.
• Prototype rapid proof-of-concepts (PoCs) using frontier GenAI (e.g., LLMs, RAG, agentic frameworks) to demonstrate technical feasibility to stakeholders, translating ambiguous business problems into tangible software solutions.
• Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, compliance and latency.
• Identify and systematically resolve the technical issues that prevent AI from reaching maturity, including data readiness gaps, system integration complexities, edge-case management, and state-management challenges.