AI Solution Architect

Key Responsibilities

• Lead the architecture, design, and technical roadmap for AI and AI-native solutions aligned to 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.