Digital Technology Architect

Senior Innovation Architect A Senior Innovation Architect is a client-facing technology leader responsible for driving high-impact innovation within enterprise organizations. The Senior Innovation Architect ensures all innovative solutions align with the client’s security requirements and enterprise architecture standards, while spearheading projects in cutting-edge domains like agentified AI, conversational AI, and enterprise automation . The position demands a strong blend of technical expertise in AI/ML and automation, enterprise system knowledge, and excellent stakeholder management skills to bridge business goals with emerging technologies. Analyze business challenges and identify use cases where emerging technologies ( AI, automation, analytics ) can deliver significant value. Solution Architecture & Alignment: Design end-to-end solutions for approved innovation use cases, creating solid architectures that balance novelty with feasibility. Ensure all solutions adhere to the client’s security policies and enterprise architecture standards (compliance with data privacy, identity management, etc. Work with client Enterprise Architecture and Security teams to obtain necessary design approvals and ensure governance compliance. Lead Innovation Pods: Lead cross-functional “innovation pods” – small, agile teams – to rapidly develop prototypes and pilot solutions i . Coordinate developers, data scientists, domain SMEs, and designers in an autonomous pod to deliver proofs-of-concept and MVPs using agile methodologies. Technical Leadership in AI & Automation: Guide projects focused on: Agentified AI: Implement AI-driven agents or autonomous processes that can perform tasks or decision-making on behalf of users. Conversational AI: Develop intelligent chatbots and voice assistants for enterprise use (customer service bots, virtual assistants, etc.). RPA, workflow orchestration, intelligent BPA) to streamline operations. Oversee the integration of innovative solutions with enterprise platforms and data sources. Work across diverse technologies – for example, embedding AI/automation into Microsoft and Google Cloud ecosystems, ServiceNow workflows, Salesforce and other CRM systems, Workday, Oracle ERP, or healthcare systems – as needed by the client environment. Ensure interoperability and data flow between new solutions and legacy systems through APIs, microservices, or middleware. Client Collaboration & Management: Act as a primary point of contact for innovation projects with clients. Manage stakeholder expectations through clear communication of project scope, timelines, and impact. Navigate enterprise processes to get innovations from idea to deployment. Work within client architecture review boards, compliance reviews, and security assessments to ensure solutions meet regulatory and data security standards (e.g. Adjust solution designs as needed to address any compliance or risk concerns. ROI Analysis & Business Case: For each innovation initiative, contribute to defining KPIs and success metrics. Help prepare business cases or ROI analyses to demonstrate the expected benefits (cost savings, efficiency gains, revenue impact) of the innovative solution. Track outcomes from pilot projects and use data to justify scaling successful innovations across the organization. Mentor junior architects and technical team members in new technologies and architecture best practices. Document insights, lessons, and reusable frameworks from projects to leverage across future innovation pods . Required Qualifications & Experience Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. Advanced degrees or relevant certifications (e.g. in AI/ML or Enterprise Architecture ) are a plus. Experience: Approximately 8–10 years of experience working with cutting-edge technologies in enterprise environments. This should include hands-on architecture or development of AI/ML solutions, intelligent automation, and systems integration projects. Prior roles could be in solution architecture, innovation labs, or enterprise architecture functions. AI/ML Expertise: Strong background in artificial intelligence and machine learning. Experience designing or implementing solutions such as predictive analytics, natural language processing, conversational AI (chatbots) , or generative AI applications. Familiarity with AI frameworks (TensorFlow, PyTorch) and AI cloud services (Azure AI, Google Vertex AI, AWS ML services, etc.). Proven experience with enterprise automation tools and integration techniques. This may include RPA platforms (e.g. Ability to design integrations via APIs, ETL pipelines, message queues, or middleware to connect heterogeneous systems. Broad understanding of enterprise application landscapes – for example, experience working with or integrating SaaS platforms like ServiceNow (ITSM workflows), Salesforce (CRM), Workday (HRIS), Oracle or SAP (ERP), and industry-specific systems (such as EHRs in healthcare). Architecture & Security Acumen: Deep knowledge of enterprise architecture principles and design patterns. Familiarity with architecture frameworks (TOGAF or similar) and cloud architecture best practices. Experience navigating architecture governance, compliance, and data security in large organizations – e.g. understanding data protection policies, identity and access management, network security basics, and regulatory constraints that affect solution design. Project Leadership: Demonstrated ability to lead technology projects or innovation initiatives end-to-end. Skilled in agile project management and comfortable overseeing fast-paced prototype development. Able to coordinate cross-functional teams (developers, analysts, UX, etc.) Proven ability to engage with both technical and non-technical stakeholders – from engineering teams to C-suite executives. Experience in managing stakeholder expectations and securing buy-in for new ideas. A creative thinker who stays updated on emerging tech trends. Experience working in ambiguous, evolving environments. AI & Machine Learning: Proficient in AI/ML concepts (deep learning, NLP, computer vision) and familiar with frameworks like TensorFlow or PyTorch. Experience with conversational AI (chatbot development) and AI agent frameworks is highly desirable. Generative AI Tools: Hands-on exposure to generative AI models (e.g. Ability to leverage large language models and AI APIs to build intelligent agents or automation. Cloud Platforms: Strong knowledge of at least one major cloud platform (Microsoft Azure, Google Cloud, or AWS). Able to use cloud services for AI (Azure Cognitive Services, GCP AI Platform, etc.), and design cloud-native architectures for scalability. Enterprise Software & Integration: Familiar with enterprise software ecosystems including CRM, ERP, ITSM, and HR systems. Skilled in integrating new solutions with existing systems via RESTful APIs, webhooks, middleware, or iPaaS tools. Experience with robotic process automation tools, workflow orchestration, and scripting. Understands how to automate data pipelines or business processes securely and reliably.