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Forward Deployed AI Engineer - Enterprise AI Architecture
Long Finch Technologies
22 minutes ago
Full-time
On-site
New York City, New York, United States
REMOTE ROLE IN US AND CANADA
ONLY W2 AND T4
Role Overview
· This is a senior, client-facing AI engineering role focused on designing and delivering enterprise-scale Generative AI, Retrieval-Augmented Generation (RAG), and agentic AI solutions from concept through production. The role combines hands-on engineering with AI architecture, multi-agent systems, context engineering, cloud integration, and human-in-the-loop operating models.
Key Responsibilities · Design, architect, and deliver enterprise-grade Generative AI, RAG, and agentic AI solutions across the full lifecycle—from discovery and proof of concept through deployment and production adoption. · Build and implement multi-agent systems, orchestration workflows, and context-engineering patterns that support reliable, scalable, and maintainable AI applications. · Develop and integrate AI solutions with enterprise platforms, cloud services, data sources, APIs, and existing business systems. · Apply responsible AI principles, including appropriate human-in-the-loop controls, governance, transparency, traceability, security, and risk-aware deployment practices · Lead technical discussions, architecture reviews, and solution-design workshops with senior client stakeholders; translate business needs into practical technical solutions. · Work hands-on with engineering teams to troubleshoot, optimize, test, and productionize AI applications for performance, reliability, and scalability. · Mentor engineers and client teams, sharing implementation guidance and helping build sustainable internal AI engineering capabilities. · Establish reusable AI architectures, engineering standards, reference patterns, and delivery practices that can be applied across enterprise engagements. · Collaborate with business, data, cloud, security, and product stakeholders to align AI solutions with enterprise requirements and measurable business objectives.
Success Measures · Successful deployment and sustained production adoption of enterprise AI solutions · Demonstrable, measurable business outcomes delivered through Generative AI, RAG, and agentic AI capabilities. · Reliable and responsible AI operations supported by appropriate governance and human-in-the-loop practices. · Effective transfer of knowledge, reusable assets, and sustainable AI capabilities to client engineering teams.
Key Responsibilities · Design, architect, and deliver enterprise-grade Generative AI, RAG, and agentic AI solutions across the full lifecycle—from discovery and proof of concept through deployment and production adoption. · Build and implement multi-agent systems, orchestration workflows, and context-engineering patterns that support reliable, scalable, and maintainable AI applications. · Develop and integrate AI solutions with enterprise platforms, cloud services, data sources, APIs, and existing business systems. · Apply responsible AI principles, including appropriate human-in-the-loop controls, governance, transparency, traceability, security, and risk-aware deployment practices · Lead technical discussions, architecture reviews, and solution-design workshops with senior client stakeholders; translate business needs into practical technical solutions. · Work hands-on with engineering teams to troubleshoot, optimize, test, and productionize AI applications for performance, reliability, and scalability. · Mentor engineers and client teams, sharing implementation guidance and helping build sustainable internal AI engineering capabilities. · Establish reusable AI architectures, engineering standards, reference patterns, and delivery practices that can be applied across enterprise engagements. · Collaborate with business, data, cloud, security, and product stakeholders to align AI solutions with enterprise requirements and measurable business objectives.
Success Measures · Successful deployment and sustained production adoption of enterprise AI solutions · Demonstrable, measurable business outcomes delivered through Generative AI, RAG, and agentic AI capabilities. · Reliable and responsible AI operations supported by appropriate governance and human-in-the-loop practices. · Effective transfer of knowledge, reusable assets, and sustainable AI capabilities to client engineering teams.