AI SOLUTION ARCHITECT

Experience: 8-15 Years Overall IT Experience, Advantive is seeking an experienced AI Solution Architect to lead the architecture, design, and delivery of enterprise AI and AI-native solutions.

The ideal candidate must come from a strong SDLC / Product Engineering background with hands-on experience in Classical Machine Learning or Deep Learning, along with strong expertise in Generative AI, LLMs, and modern AI tools.

The architect will work closely with business stakeholders, product owners, data teams, and engineering teams to design scalable, secure, and production-ready AI solutions.

MANDATORY SKILLS

• AI Solution Architecture
• AI Solution Design
• Enterprise AI Architecture
• Latest AI Models
• Generative AI / GenAI
• Large Language Models (LLMs)
• Machine Learning / Deep Learning
• Cloud AI/ML Platforms
• Data & MLOps Integration
• SDLC / Product Engineering
• Enterprise AI Solution Delivery, * Lead architecture and technical roadmap for AI and AI-native solutions.
• Translate business requirements into scalable AI architectures covering data, model, application, and integration layers.
• Evaluate and integrate modern AI models and LLMs into enterprise applications.
• Design reference architectures, reusable patterns, standards, and AI components.
• Collaborate with Data Engineers, MLOps Engineers, Application Developers, and Product Teams.
• Define requirements for performance, security, reliability, and observability.
• Conduct technical reviews, PoCs, and feasibility assessments for AI use cases.
• Create HLDs, LLDs, sequence diagrams, and data-flow diagrams.
• Provide technical leadership and mentoring to engineering teams.

AI Architecture Experience: 3-5+ Years, * 8-15 years of overall IT experience with 3-5+ years in AI Solution Architecture.

• Strong experience designing and delivering enterprise-grade AI solutions.
• Hands-on experience with Machine Learning, Deep Learning, and LLM-based solutions.
• Strong understanding of modern AI models, LLM capabilities, limitations, and use cases.
• Experience with Azure AI, AWS AI/ML, Google Cloud AI, or equivalent platforms.
• Understanding of data pipelines, feature stores, model deployment, and MLOps.
• Strong knowledge of APIs, microservices, and event-driven architecture.
• Experience integrating AI into enterprise products and workflows.
• Strong communication, stakeholder management, and technical leadership skills.
• Bachelor’s or Master’s degree in Computer Science, IT, Engineering, or related field., * Responsible AI and AI Governance
• Model Risk Management
• Vector Databases
• RAG / Retrieval-Augmented Generation
• Semantic Search
• Knowledge Graphs
• MLOps / ML CI/CD
• Docker / Kubernetes
• Cloud-Native Architecture
• ERP / CRM Integration
• Product-based / ISV experience
• Agile delivery experience, Candidates should have demonstrated experience taking AI solutions from architecture and design through production implementation, rather than only working with AI tools or performing research/POC activities.