Senior AI Solutions Architect

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior AI Solutions Architect based in India. As a Senior AI Solutions Architect, you will shape and validate enterprise AI solutions, ensuring they are technically robust, commercially viable, secure, and ready for successful delivery. You will serve as a key link between sales, regional solution teams, and global AI delivery specialists, translating complex business challenges into scalable, production-ready architectures. From generative AI and agentic AI to cloud integration and enterprise data platforms, you will guide solution design, feasibility assessments, and proof-of-concept development. You will also play a critical role in deal assurance, validating delivery scope, resource requirements, pricing assumptions, and contractual commitments before projects move forward. Working with multidisciplinary teams and senior stakeholders, you will help minimize delivery risks while protecting project quality and profitability. This remote opportunity offers the chance to influence AI strategy, strengthen delivery governance, and contribute to reusable architectures and best practices across a global technology environment.

Accountabilities

• AI opportunity assessment and stakeholder alignment: Act as the primary technical and delivery contact for assigned AI opportunities throughout the sales lifecycle, from initial qualification and discovery through solution approval, contracting, and handover. Collaborate with sales, go-to-market, commercial, security, legal, and delivery teams to clarify business objectives, technical requirements, data constraints, and expected outcomes.
• Enterprise AI architecture and solution design: Define and validate end-to-end architectures spanning applications, AI models, data pipelines, integrations, cloud infrastructure, orchestration frameworks, and security. Select appropriate technologies and architectural patterns based on performance, scalability, cost, compliance, and delivery complexity.
• Prototyping and technical feasibility: Lead or coordinate prototypes and proofs of concept to validate critical assumptions, demonstrate business value, and reduce implementation risks. Evaluate model performance, data quality, integration requirements, security, latency, scalability, and operating costs, translating findings into production-ready recommendations.
• Delivery planning and resource management: Develop realistic delivery approaches, including workstreams, milestones, dependencies, resource requirements, skills, delivery locations, and mobilization plans. Define appropriate governance structures, responsibilities, escalation processes, change controls, and ongoing support requirements.
• Commercial modeling and financial validation: Partner with sales, finance, commercial, and delivery stakeholders to develop viable pricing and cost models. Validate effort estimates, resource rates, third-party expenses, cloud consumption costs, contingency, cost recovery, and margin assumptions to ensure commercial commitments align with the proposed architecture and delivery model.
• Scope definition and contractual documentation: Contribute to proposals and statements of work by clearly documenting deliverables, milestones, assumptions, exclusions, acceptance criteria, client responsibilities, and dependencies. Coordinate legal, security, privacy, and responsible AI reviews to ensure contractual commitments accurately reflect the approved solution.
• Deal assurance and risk governance: Evaluate solution completeness, technical feasibility, delivery readiness, commercial consistency, and risk mitigation. Identify material technical, security, privacy, data, legal, contractual, and operational risks, coordinate approval processes, and provide evidence-based recommendations to proceed, proceed conditionally, revise, or reject a proposed solution.
• Delivery transition and mobilization: Maintain an approved solution baseline throughout contracting and ensure a structured handover to delivery teams. Communicate the architecture, scope, estimates, resource plan, commercial assumptions, risks, dependencies, and client commitments, supporting early mobilization and resolving discrepancies between proposed and delivered solutions.
• Knowledge sharing and continuous improvement: Develop reusable AI architectures, delivery patterns, prototypes, estimation…