Senior AI Solutions Architect

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 models, proposal templates, and commercial frameworks. Coach regional teams, share lessons learned, and improve the methods and governance practices used for AI opportunity assessment and assurance. Requirements: Education: A bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, Business, or a related field, or equivalent practical experience. Professional experience: Extensive experience in enterprise solution architecture, technical consulting, AI engineering, or complex technology delivery, preferably within a large technology services or enterprise consulting environment. Enterprise architecture expertise: Advanced knowledge of application, integration, data, infrastructure, cloud, and security architecture, with the ability to design solutions that meet enterprise requirements and align with established architecture standards. AI and generative AI proficiency: Strong understanding of AI, generative AI, and agentic AI solution patterns, including model selection and evaluation, retrieval-augmented generation (RAG), prompt engineering, grounding, data pipelines, APIs, vector databases, and orchestration frameworks. Cloud and engineering knowledge: Experience with one or more major cloud platforms, such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP), including their AI, data, integration, and security capabilities. Familiarity with software engineering, DevOps, MLOps, and LLMOps is important. Technical validation and production readiness: Demonstrated ability to develop or oversee prototypes, validate technical feasibility, and define the path from proof of concept to secure, scalable, maintainable production solutions integrated with enterprise platforms and data. Delivery planning and estimation: Experience defining project scope, work breakdown structures, resource models, effort estimates, delivery timelines, dependencies, governance, and mobilization plans for complex technology engagements. Commercial and contractual expertise: Practical experience developing or validating pricing models, rate-card assumptions, third-party costs, financial estimates, contribution margins, and business cases. Familiarity with statements of work, acceptance criteria, contractual assumptions, change control, and delivery risk management is essential. Security, governance, and responsible AI: Strong understanding of security, privacy, regulatory compliance, data governance, and responsible AI principles, with the ability to identify risks and establish appropriate controls. Client-facing consulting and communication: Excellent communication, presentation, and stakeholder-management skills, with the ability to translate complex technical concepts into clear recommendations for technical teams, commercial stakeholders, executives, and clients. Analytical and decision-making abilities: Strong attention to detail, sound judgment, and the ability to challenge assumptions constructively, assess complex information objectively, and make evidence-based decisions under demanding deadlines. Cross-functional collaboration: Proven ability to coordinate effectively across sales, engineering, delivery, finance, legal, security, and partner teams while managing multiple complex opportunities simultaneously. Qualifications and certifications: Relevant certifications from Microsoft Azure, AWS, or Google Cloud are preferred. Enterprise or solution architecture certifications such as TOGAF are advantageous, as are additional qualifications in AI, security, privacy, agile methodologies, project management, or service management. Additional experience: Experience in regulated industries, AI managed services, production model operations, and formal solution or deal-assurance reviews would be an advantage. Benefits: Remote working: Flexibility to work remotely from India. Full-time opportunity: A full-time position focused on enterprise AI architecture and global technology engagements. High-impact projects: Contribute to complex AI initiatives involving generative AI, agentic AI, cloud platforms, enterprise data, and production-scale solutions. Global collaboration: Work with international stakeholders across sales, engineering, commercial, security, and delivery teams. Professional growth: Expand your expertise in enterprise AI architecture, solution assurance, commercial strategy, and large-scale technology delivery. Innovation and knowledge sharing: Contribute to reusable AI frameworks, architectural standards, prototypes, and continuous improvement initiatives. Career development: Opportunities to strengthen technical leadership, client advisory, and cross-functional decision-making skills. Salary and additional benefits: Compensation, healthcare coverage, paid leave, bonuses, and other employment benefits have not been specified in the available job details and should be confirmed during the recruitment process.