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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Solutions Architect - Generative AI & Enterprise AI Strategy - **Company:** ApTask - **Location:** New York, NY, United States (Remote available) - **Experience:** Experienced - **Salary:** $160,000.0 - $175,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Encodings, Graph Database, Monitoring of Systems, Python (Programming Language), Knowledge Management, Search Technologies, Software Deployment, Enterprise Software Applications, Large Language Models, Multi-Agent Systems, Prompt Engineering, IT Architecture, Model Validation, Generative AI, Event Driven Architecture, AI Platforms, Kubernetes, Information Technology, Machine Learning Operations, Virtual Agents, Restful APIs, GPT, Automation Anywhere, Docker, Microservices - **Published:** August 16, 2026 - **Apply:** https://www2.jobdiva.com/portal/?a=4ujdnwqsdebu7m13em5f0pt5dw80o500d7dv9cbq5ebzngb7yk0n43mjtefnbx0d&compid=0/jobs/29043983#/jobs/29043983 ## About the Role * 15+ years of overall IT experience. * 7+ years in Solution Architecture or Enterprise Architecture. * 3+ years of hands-on experience designing and delivering Generative AI solutions in enterprise environments. Strong expertise in: * Generative AI * Large Language Models (LLMs) * Agentic AI * Multi-Agent Systems * AI Copilots * Prompt Engineering * Prompt Chaining * AI Reasoning Workflows Experience working with: * OpenAI GPT * Claude * Gemini * Llama * Mistral * Cohere Deep understanding of: * Retrieval-Augmented Generation (RAG) * GraphRAG * Vector Databases * Embeddings * Semantic Search * Knowledge Graphs Hands-on experience with: * LangChain * LangGraph * LlamaIndex * Semantic Kernel * CrewAI * AutoGen * MCP (Model Context Protocol) Experience building AI solutions on: * Azure AI Foundry * Azure OpenAI Service * AWS Bedrock * Google Vertex AI Strong knowledge of: * Python * REST APIs * Microservices * Docker * Kubernetes Experience with: * AI Governance * Responsible AI * LLMOps * Model Monitoring * Model Evaluation * AI Security * AI Compliance * Excellent consulting, communication, and executive stakeholder management skills. ## Description * We are seeking an experienced AI Solutions Architect with 15+ years of IT experience to lead the design, architecture, and implementation of enterprise-scale Artificial Intelligence solutions. * The ideal candidate will possess deep expertise in Generative AI (GenAI), Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), AI Governance, and Enterprise AI Architecture, with a proven ability to drive AI adoption across enterprise business functions. * This role requires close collaboration with executive leadership, business stakeholders, product teams, and engineering organizations to build scalable, secure, and responsible AI solutions that accelerate business transformation., * Define and execute enterprise AI strategy, architecture, and technology roadmap aligned with business objectives. * Design and architect scalable Generative AI, Agentic AI, AI Copilots, Retrieval-Augmented Generation (RAG), GraphRAG, and Knowledge Management solutions. * Evaluate, benchmark, and recommend appropriate LLMs including OpenAI GPT, Claude, Gemini, Llama, Mistral, Cohere, and other emerging foundation models. * Design AI applications utilizing Multi-Agent Systems, autonomous AI workflows, and intelligent orchestration frameworks. * Build enterprise AI architectures leveraging Azure AI Foundry, Azure OpenAI, AWS Bedrock, Google Vertex AI, and cloud-native AI services. * Lead architecture for LLMOps, model deployment, prompt management, AI monitoring, model evaluation, governance, and lifecycle management. * Design enterprise semantic search, vector search, embedding pipelines, and retrieval architectures using modern vector databases. * Establish AI governance, Responsible AI, security, compliance, privacy, and risk management frameworks. * Collaborate with engineering teams to integrate AI capabilities into enterprise applications using APIs, microservices, and event-driven architectures. * Optimize AI solution performance, inference latency, token utilization, scalability, and operational costs. * Provide technical leadership throughout the AI solution lifecycle from discovery and proof of concept through production deployment. * Partner with executive stakeholders to identify AI use cases, define business value, and lead enterprise AI transformation initiatives. * Mentor architects and engineering teams on AI architecture best practices and emerging technologies. ## Related Videos - [Should we build Generative AI into our existing software?](https://www.wearedevelopers.com/videos/1129-should-we-build-generative-ai-into-our-existing-software) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [A Brief History of Data Storage](https://www.wearedevelopers.com/videos/974-a-brief-history-of-data-storage) - [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) - [Streaming AI Responses in Real-Time with SSE in Next.js & NestJS](https://www.wearedevelopers.com/videos/1630-streaming-ai-responses-in-real-time-with-sse-in-next-js-nestjs) ## Related Articles - [Got AI ideas but no money? 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