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Solution Architect - Agentic AI & Data

INUIX ConsultingNew York, NY🇺🇸United StatesPosted 5 Aug 2026

Why This Role Stands Out

This hybrid role offers a unique opportunity to architect cutting-edge Agentic AI and Data solutions, fostering significant career growth within a reputable consulting firm. You'll thrive here if you possess deep technical expertise in AI/ML, cloud, and data, combined with strong leadership and problem-solving skills, allowing you to drive innovation for clients.

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
New York, NY, United States
Posted
3 weeks ago
AWSEncryptionMLOpsMachine LearningAgileAzureGPTGenerative AIGoogle CloudHugging FacePythonStakeholder Management

Job Description

Solution Architect - Agentic AI & Data 
New York, NY or Edison, NJ
Exp : 13 to 25 years
 
What Skills Are Expected
 
•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.

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