AI Solutions Architect

Role: AI Solution Architect

Location: Remote

Duration: Fulltime

Experience: 8–15 years of overall IT experience, with at least 3–5 years focused on AI solution architecture and delivery. 

Mandatory Skill Tags: AI Solution Architecture, AI solution design, latest AI models, LLMs, enterprise AI architecture, cloud AI/ML platforms, data & MLOps integration 

Secondary Skill Tags: responsible AI, AI governance, vector databases, RAG, semantic search, MLOps tools, cloud-native architecture, microservices, Kubernetes, agile delivery

Job Summary:

The Onsite AI Solution Architect will lead the end-to-end architecture, design, and implementation of AI and AI-native solutions for Advantive. This role will closely collaborate with business stakeholders, product owners, data teams, and engineering to translate business requirements into scalable, secure, and robust AI architectures. The architect will provide thought leadership on latest AI models and LLMs and ensure best practices, governance, and standards are adopted across AI initiatives. 

Key Responsibilities:

• Lead the architecture, design, and technical roadmap for AI and AI-native solutions aligned to Advantive’s business strategy. 
• Translate business and functional requirements into scalable AI solution architectures, covering data, model, application, and integration layers. 
• Evaluate, select, and integrate latest AI models and LLMs (including cloud and third-party services) into enterprise applications and workflows. 
• Define reference architectures, patterns, standards, and reusable components for AI solution delivery across the organization. 
• Collaborate with data engineers, MLOps engineers, application developers, and product teams to ensure high-quality, production-grade AI deployments. 
• Establish non-functional requirements (performance, security, reliability, observability) and ensure AI solutions meet enterprise architecture and compliance guidelines. 
• Conduct technical reviews, PoCs, and feasibility assessments for new AI use cases and guide teams on best practices and optimization. 
• Provide architectural leadership, mentoring, and guidance to project teams, driving continuous improvement and innovation in AI solution delivery. 

Required Skills:

• Strong experience in AI Solution Architecture, designing and delivering enterprise-grade AI solutions. 
• Proven expertise in architectural design involving AI solutions, including end-to-end solution blueprints and reference architectures.
• Hands-on knowledge of designing AI-based solutions using machine learning, deep learning, and LLM-based approaches. 
• In-depth understanding of latest AI models and large language models (LLMs), including their capabilities, limitations, and suitable use cases. 
• Experience with AI/ML platforms and services (e.g., Azure AI, AWS AI/ML, Google Cloud AI, or equivalent). 
• Solid understanding of data architecture concepts, including data pipelines, feature stores, model deployment, and monitoring (MLOps). 
• Strong background in application integration patterns (APIs, microservices, event-driven architecture) for embedding AI into products and workflows. 
• Ability to create high-quality architectural artifacts (HLDs, LLDs, sequence diagrams, data flow diagrams) and communicate them to technical and non-technical stakeholders. 
• Strong stakeholder management, communication, and leadership skills to drive consensus and decision-making. 

Good to Have Skills

• Experience with AI governance, model risk management, and responsible AI practices (fairness, explainability, security, and privacy). 
• Familiarity with vector databases, semantic search, RAG (Retrieval-Augmented Generation), and knowledge-graph-based solutions. 
• Exposure to MLOps tools and frameworks for CI/CD of ML models and LLM-based applications. 
• Experience in designing multi-tenant, cloud-native architectures using containers and orchestration (Docker, Kubernetes). 
• Knowledge of enterprise integration with ERP/CRM/line-of-business applications. 
• Prior experience in leading AI architecture for product-based or ISV organizations. 
• Experience working in agile delivery environments and collaborating with distributed teams. 

Educational Qualification

Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related discipline from a recognized institution.