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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Solution Architect - Agentic AI & Data - **Company:** INUIX Consulting - **Location:** Edison, NJ, United States - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, Microsoft Azure, Software as a Service, Cloud Computing, Cloud Engineering, Computer Programming, Data Architecture, Data Infrastructure, Relational Databases, DevOps, Distributed Systems, Middleware, Graph Database, Identity and Access Management, Python (Programming Language), Machine Learning, Message Broker, NoSQL, Cloud Services, Tensorflow, Search Technologies, UML, Reinforcement Learning, Scripting, Google Cloud, Cloud Platform System, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Event Driven Architecture, Containerization, Data Lakes, AI Platforms, HuggingFace, Atlassian Tools, Enterprise Integration, Integration Frameworks, Data Management, Machine Learning Operations, Virtual Agents, GPT, Data Pipelines, Serverless Computing - **Published:** August 6, 2026 - **Apply:** https://www.dice.com/job-detail/ae148481-072a-4c1c-9cae-4d764a3287e8 ## About the Role *AI/ML Solution Architecture: Extensive experience in designing and architecting AI or machine learning solutions in an enterprise context. *Deep Technical Knowledge: Strong understanding of machine learning and AI techniques, especially Generative AI and large language models. *Multi-Agent System Design: Knowledge of multi-agent system patterns and frameworks. *Prompt Engineering & RAG: Ability to craft effective prompts and chaining strategies for LLMs, familiar with retrieval-augmented generation methods. *AI Ethics & Responsible AI: Strong grasp of AI ethics and safety principles, able to identify ethical risks and design mitigations. *Cloud & Distributed Systems: Deep understanding of cloud architecture and distributed system design. *Data Management: Solid understanding of data architecture as it relates to AI, including data pipelines, d atabases, and data lakes. *Leadership & Communication: Excellent communication and stakeholder management skills, capable of leading discussions with C-level executives and technical brainstorming with engineers. *Consulting and Domain Acumen: Prior consulting or client-facing experience, adept at requirement gathering and crafting proposals. *Problem-Solving & Innovation: Creative mindset to devise innovative solutions leveraging AI agents, strong problem-solving skills. *Continuous Learning: Demonstrated habit of continuous learning, staying updated via research papers, conferences, or hands-on experimentation. *Banking, Financial Services and Insurance domain knowledge will be a plus Key Technology Capabilities *AI & ML Frameworks: Familiarity with major AI/ML frameworks and services, including OpenAI GPT models, Google PaLM/Vertex AI, and Hugging Face Transformers library. *SaaS AI & Data Platforms: Experience with leading SaaS AI & Data platforms in terms of agentic AI development, implementation, orchestration, AI guardrails *Agentic AI Tooling: Exposure to frameworks and libraries for building AI agents and chains, such as LangChain ,Microsoft's Semantic Kernel. *Retrieval Systems: Strong knowledge of search and retrieval technologies, including vector databases and semantic search. *Cloud Services: Expertise in cloud ecosystems (AWS, Azure, Google Cloud Platform), including cloud AI services, serverless computing, containerization, and related DevOps tools. *Programming & Scripting: Proficiency in programming languages commonly used for AI and integration, primarily Python and at least one general-purpose language. *Data Platforms: Knowledge of modern data platforms, including relational databases, NoSQL stores, and data processing frameworks. *Integration & APIs: Experience designing and using APIs and middleware, knowledge of event-driven architectures and message brokers. *DevOps & MLOps: Familiar with CI/CD pipelines and infrastructure as code, understanding of MLOps principles and tools. *Security & Compliance Tools: Comfort with technologies for securing AI applications, including identity and access management, encryption, and compliance tools. *Collaboration & Design: Proficient with tools used in architecture and design documentation, including UML design tools and agile project management tools. *Emerging Tech: Awareness of emerging tech such as knowledge graphs and reinforcement learning frameworks. ## Related Videos - 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