AI Solution Architect - Data & AI

The role will support business growth by shaping high-quality proposal responses, solution blueprints, architecture diagrams, AI adoption roadmaps, reusable solution assets, implementation approaches, and transformation narratives aligned to client business outcomes.\n\nKey Responsibilities\nLead and contribute to end-to-end presales solutioning activities for AI, ML, GenAI, Agentic AI, enterprise AI platforms, AI governance, and AI adoption opportunities.\nAnalyze client RFPs, RFIs, RFQs, business requirements, current-state AI/data landscape, pain points, use cases, compliance needs, and evaluation criteria to design fit-for-purpose AI solutions.\nCreate solution responses covering technical approach, target architecture, AI solution design, data and model architecture, integration approach, operating model, implementation roadmap, assumptions, dependencies, risks, and differentiators.\nArchitect enterprise AI solutions covering machine learning, deep learning, natural language processing, computer vision, predictive analytics, recommendation engines, intelligent automation, GenAI applications, custom copilots, conversational AI, content intelligence, knowledge assistants, and Agentic AI workflows.\nDevelop proposal-ready architecture narratives, logical architecture diagrams, AI reference architectures, solution flow diagrams, deployment views, AI operating model views, and technology stack recommendations.\nSupport solution estimation by defining work breakdown structures, delivery phases, team composition, assumptions, dependencies, accelerators, and high-level effort inputs.\nCollaborate with delivery teams to ensure proposed AI solutions are practical, scalable, secure, compliant, implementable, and aligned with HCLTech delivery capabilities.\nParticipate in client workshops, discovery sessions, AI ideation sessions, use case prioritization workshops, solution walkthroughs, technical presentations, and proposal defense discussions.\nContribute to reusable assets, solution playbooks, reference architectures, estimation models, proposal templates, AI use case catalogs, GenAI patterns, governance frameworks, and industry-specific AI solution accelerators.\nWork with partner ecosystems including hyperscalers, foundation model providers, AI/ML platforms, data platforms, automation platforms, analytics platforms, and governance/security technology partners.\nStay updated on emerging trends in Artificial Intelligence, Generative AI, Agentic AI, multimodal AI, responsible AI, AI governance, LLMOps, ModelOps, AI observability, AI testing, synthetic data, vector databases, RAG architectures, and enterprise AI adoption patterns.\n\nPrincipal KPIs for the Role\nContribution to strategic deal wins\nQuality and timeliness of RFP/RFI responses\nCustomer solution acceptance and feedback\nReusable solution assets/frameworks contributed\nEffectiveness of proposed AI architecture, AI governance, and AI adoption approach.\nAlignment of solutions to customer business outcomes, risk controls, compliance needs, and enterprise AI strategy.\nInnovation and value-added solution recommendations\n\nCore Competencies & Technical Expertise\nStrong expertise in Data & AI solutioning and architecture for enterprise customers.\nHands-on experience in preparing solution responses, architecture documents, presentations, technical proposals, AI strategy documents, operating models, and implementation roadmaps.\nExperience responding to RFPs/RFIs/RFQs and creating solution narratives, architecture diagrams, delivery approaches, AI adoption models, governance models, and estimation inputs.\nStrong understanding of modern AI ecosystems including Machine Learning, Deep Learning, Generative AI, Agentic AI, Natural Language Processing, Computer Vision, Predictive Analytics, Conversational AI, Intelligent Search, AI-infused BI, and AI-enabled automation.\nExperience with cloud platforms such as Microsoft Azure, AWS, and Google Cloud Platform, especially in the context of AI/ML, GenAI, data platforms, model deployment, observability, security, and governance.\nStrong understanding of cloud-native AI services across:\nAzure: Azure Machine Learning, Azure AI Foundry, Azure OpenAI Service, Azure AI Search, Azure AI Services\nAWS: Amazon SageMaker, Amazon Bedrock, Amazon Q, Amazon Comprehend, Amazon Textract, Amazon Rekognition, Amazon Transcribe, Amazon Translate, AWS AI governance/security services.\nGCP: Vertex AI, Gemini models, BigQuery ML, Document AI, Vision AI, Natural Language AI, Dialogflow\nStrong knowledge of AI architecture patterns including retrieval augmented generation, prompt engineering, fine-tuning, model evaluation, model selection, multi-agent workflows, human-in-the-loop review, AI orchestration, API integration, knowledge grounding, semantic search, and enterprise AI integration.\nAbility to define enterprise AI operating models including AI Center of Excellence, shared AI platform services, use case intake, prioritization, governance boards, adoption management, AI literacy, change management, and platform operations.\nStrong communication and presentation skills with experience interacting with customer stakeholders, CIO/CDO/CAIO organizations, business leaders, technology leaders, data teams, risk teams, and compliance stakeholders.\nAbility to work independently across multiple concurrent opportunities in a fast-paced presales environment.\nStrong analytical thinking, problem-solving, consulting, storytelling, and executive communication skills.\n\nRequired Qualifications\nBachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.\nPreferred Certifications: Certifications across Azure AI, AWS AI/ML/GenAI, Google Cloud ML/AI, Databricks GenAI/ML, Snowflake GenAI, and Responsible AI/Governance are strongly preferred.\nStrong hands-on and architectural understanding of AI/ML platforms, GenAI solutions, model lifecycle management, cloud AI services, AI governance, MLOps, LLMOps, and enterprise AI adoption patterns.\nExperience in presales solutioning, proposal development, consulting, or customer-facing architecture roles is strongly preferred.\nMinimum 10 years of experience in Data & AI technologies, with relevant experience in AI architecture, ML engineering, GenAI solutioning, solution architecture, and presales solutioning.