AI Architect

Senior Innovation Architect

Senior Innovation Architect

Key Responsibilities

• Innovation Use Case Identification:

  Engage with client stakeholders to uncover and prioritize high-impact innovation opportunities. Analyze business challenges and

  identify use cases

  where emerging technologies (

  AI, automation, analytics

  ) can deliver significant value. Develop innovation roadmaps aligned to client business goals.

• 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 solutionsi. Coordinate developers, data scientists, domain SMEs, and designers in an autonomous pod to deliver proofs-of-concept and MVPs using agile methodologies. Iterate on solutions based on feedback and evolving requirements.

• Technical Leadership in AI & Automation:

  Serve as the

  subject matter expert

  in emerging technologies. 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.).
• Enterprise Automation: Drive process automation initiatives (e.g. RPA, workflow orchestration, intelligent BPA) to streamline operations.
• Provide hands-on technical direction and architectural guidance in these domains, ensuring solutions are scalable and integrate with existing systems.

• Cross-Platform Integration:

  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. Facilitate workshops and ideation sessions to shape solution concepts. Manage stakeholder expectations through clear communication of project scope, timelines, and impact. Provide regular updates and demonstrations of progress to client executives. Ensure strong client buy-in and change management for adopting new innovations.

• Alignment with Compliance & Standards:

  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. GDPR, HIPAA in healthcare, ISO 27001) and adhere to IT governance. 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.

• Mentoring & Knowledge Sharing:

  Mentor junior architects and technical team members in new technologies and architecture best practices. Promote a culture of innovation and experimentation within both the internal team and the client organization. Document insights, lessons, and reusable frameworks from projects to

  leverage across future innovation pods

  .

Required Qualifications & Experience

• Education:

  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.).

• Automation & Integration:

  Proven experience with enterprise automation tools and integration techniques. This may include RPA platforms (e.g. UiPath, Automation Anywhere), workflow automation, business process management suites, or custom scripting to integrate systems. Ability to design integrations via APIs, ETL pipelines, message queues, or middleware to connect heterogeneous systems.

• Enterprise Systems Knowledge:

  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). Able to quickly learn new platforms’ capabilities and constraints to design compatible innovative solutions.

• 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.) and external vendors if required.

• Communication & Stakeholder Management:

  Excellent client-facing communication skills. Proven ability to

  engage with both technical and non-technical stakeholders

  – from engineering teams to C-suite executives. Strong presentation and storytelling skills to articulate vision and technical concepts in business terms. Experience in

  managing stakeholder expectations

  and securing buy-in for new ideas.

• Problem-Solving & Innovation Mindset:

  A creative thinker who stays updated on emerging tech trends. Passion for experimenting with new tools and approaches. Comfortable with a “fail-fast” mindset to quickly prototype, learn, and iterate. Strong analytical and problem-solving skills to devise innovative solutions to complex business problems.

• Adaptability:

  Experience working in ambiguous, evolving environments. Ability to adapt to different industries and client contexts quickly. Enthusiasm for continuous learning and upskilling in fast-moving technology domains.

Skills Checklist

• 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. GPT-4, DALL·E) and their applications. 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.

• Automation Technologies:

  Experience with robotic process automation tools, workflow orchestration, and scripting. Understands how to automate data pipelines or business processes securely and reliably.