Forward Deployed AI Engineer – Enterprise AI Architecture

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.

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