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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Enterprise Architect - Generative AI - **Company:** ApTask - **Location:** New York, NY, United States - **Salary:** $220,000.0 - $240,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Engineering, Information Engineering, Distributed Systems, Monitoring of Systems, Performance Tuning, Search Technologies, Enterprise Data Management, Enterprise Application Integration, Google Cloud, Enterprise Software Applications, Large Language Models, Multi-Agent Systems, Prompt Engineering, IT Architecture, Generative AI, Data Lakes, AI Platforms, HuggingFace, Data Management, Machine Learning Operations, Api Design, Docker, Microservices - **Published:** July 7, 2026 - **Apply:** https://www2.jobdiva.com/portal/?a=4ujdnwqsdebu7m13em5f0pt5dw80o500d7dv9cbq5ebzngb7yk0n43mjtefnbx0d&compid=0/jobs/28812752#/jobs/28812752 ## About the Role * 12+ years of experience in Enterprise Architecture, Solution Architecture, or AI Platform Engineering. * Strong experience with Generative AI technologies, including LLMs, prompt engineering, RAG pipelines, and vector databases. * Hands-on experience with AI frameworks such as LangChain, Semantic Kernel, LlamaIndex, Hugging Face, or OpenAI ecosystems. * Expertise in cloud platforms including AWS, Azure, or Google Cloud Platform. * Experience designing microservices and API-driven architectures using modern development frameworks. * Strong understanding of AI security, governance, compliance, and responsible AI principles. * Experience with containerization and orchestration technologies such as Docker and Kubernetes. * Knowledge of data engineering, enterprise integration patterns, and distributed systems. * Strong stakeholder management, communication, and leadership skills. * Experience working in regulated enterprise environments is highly preferred. Preferred Qualifications: * Experience implementing AI governance and model risk management frameworks. * Exposure to MLOps, AI observability, and model monitoring tools. * Familiarity with enterprise data platforms, data lakes, and vector search technologies. * Relevant cloud or AI certifications preferred. ## Description * We are seeking an experienced Enterprise Architect - Generative AI to lead the design and implementation of enterprise-scale AI solutions across complex business environments. * The ideal candidate will drive GenAI architecture strategy, define AI governance standards, and build secure, scalable, and compliant AI platforms leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and cloud-native AI services. * This role requires strong expertise in enterprise architecture, AI/ML ecosystems, cloud platforms, security, and regulatory compliance. * The candidate will collaborate with business stakeholders, engineering teams, risk, legal, and compliance groups to deliver innovative AI-driven solutions aligned with organizational standards and governance frameworks., * Lead enterprise-wide Generative AI architecture strategy and roadmap initiatives. * Design and implement scalable AI solutions using LLMs, RAG frameworks, vector databases, and AI orchestration platforms. * Define architecture standards, security controls, governance policies, and responsible AI practices. * Partner with business leaders and technical teams to identify AI use cases and translate them into enterprise solutions. * Architect cloud-native AI platforms using AWS, Azure, or GCP services. * Develop secure AI integration patterns with enterprise applications, APIs, and data platforms. * Collaborate with cybersecurity, legal, risk, and compliance teams to ensure regulatory adherence and data privacy standards. * Evaluate emerging AI technologies, frameworks, and tooling for enterprise adoption. * Provide technical leadership, architecture guidance, and mentorship to engineering and solution teams. * Support AI model lifecycle management, monitoring, observability, and performance optimization. * Drive AI platform scalability, reliability, and operational excellence across enterprise environments. ## Related Videos - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [API Design - Getting Started](https://www.wearedevelopers.com/videos/33-api-design-getting-started) - [Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow](https://www.wearedevelopers.com/videos/1525-beyond-gpt-building-unified-genai-platforms-for-the-enterprise-of-tomorrow) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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