AI Solution Specialist I
- India
Posted 3 days ago
About the role
| Bengaluru, KarnatakaNoida, Uttar Pradesh
Job Summary
The AI Solution Specialist partners with clients, engineering teams, enterprise architects, sales and alliance stakeholders to shape commercially viable and technically credible OpenAI-enabled solutions for defined pursuits and transformation opportunities. Translates customer challenges, business processes, and desired outcomes into differentiated solution narratives, architecture constructs, implementation scopes, assumptions, commercials, and delivery roadmaps. Acts as the bridge between client ambition and engineering feasibility, ensuring proposals reflect realistic OpenAI capabilities, enterprise integration requirements, AI lifecycle considerations, responsible AI controls, and proven delivery patterns. Apply strong techno-functional knowledge of OpenAI, hyperscale ecosystems, enterprise technology platforms, HCLTech capabilities, and partner co-sell frameworks to create scalable, secure, and outcome-oriented solutions.
Key Responsibilities
Discover and translate client business problems into high-value OpenAI use cases, solution narratives, target outcomes, and measurable success criteria.
Shape pursuit artifacts including the solution vision, logical architecture, implementation scope, assumptions, exclusions, dependencies, risks, operating model, roadmap, and high-level commercial inputs.
Collaborate with engineering and architecture teams to validate application feasibility, model suitability, integration complexity, data readiness, security requirements, and production constraints.
Define an evaluation-led solution approach covering baseline performance, acceptance criteria, quality thresholds, safety testing, user validation, and continuous improvement.
Ensure proposals account for the complete AI solution lifecycle, including discovery, prototyping, evaluation, industrialization, deployment, governance, monitoring, adoption, and optimization.
Align OpenAI solution designs hyperscale environments such as Microsoft Azure, AWS, and Google Cloud, as well as relevant enterprise platforms, data services, security controls, and systems of record.
Incorporate applicable technologies and offerings from strategic OEMs and partners while maintaining clear architectural accountability and avoiding unnecessary solution complexity.
Apply HCLTech reference architectures, delivery playbooks, reusable accelerators, industry assets, and commercial constructs to improve pursuit speed, consistency, differentiation, and win probability.
Facilitate technical alignment and solution-shaping workshops with client stakeholders, delivery leaders, engineers, security teams, alliance partners, and commercial teams.
Develop credible sizing, effort, capacity, consumption, and pricing inputs in collaboration with engineering, delivery, finance, and commercial stakeholders.
Articulate solution differentiation based on business value, implementation feasibility, responsible AI, enterprise governance, time to value, and the ability to scale from pilot to production.
Skill Requirements
The following AI skills are mandatory for this role:
Strong AI and LLM fundamentals, including prompt and context engineering, embeddings, RAG, vector search, agentic workflows, orchestration, model selection, evaluations, and AI lifecycle considerations.
Working knowledge of OpenAI or Azure OpenAI APIs and platform capabilities, ChatGPT Enterprise use cases, OpenAI Codex or comparable AI-assisted engineering tools, and relevant hyperscale AI services.
Ability to identify and prioritize high-value AI use cases through client discovery, process assessment, data-readiness analysis, technical feasibility, value mapping, and measurable business-outcome definition.
Ability to shape end-to-end AI solutions covering target architecture, enterprise integration, delivery scope, effort and consumption estimates, implementation roadmap, operating model, adoption, and pilot-to-production scaling.
Ability to incorporate evaluation criteria, Responsible AI, security, privacy, governance, safety guardrails, human oversight, observability, and operational controls into proposals and solution designs.
Other Requirements
5+ years of experience in presales, GTM solutioning, solution architecture, technology consulting, or digital transformation.
Proven experience leading client discovery and solution-shaping workshops, prioritizing use cases, developing value propositions, and translating business requirements into differentiated and technically credible AI solutions.
Experience developing solution narratives, architecture blueprints, delivery scope, assumptions, dependencies, risks, effort and consumption estimates, implementation roadmaps, commercials, and business cases.
Experience contributing to RFI/RFP responses, proposals, presentations, demonstrations, POCs, pilots, executive briefings, and deal reviews in collaboration with sales, engineering, delivery, finance, and alliance teams.
Strong client-facing communication, storytelling, negotiation, and stakeholder-management skills, supported by a sound understanding of cloud, data, integration, security, governance, and production AI requirements.
Preferred Qualifications
Experience with Azure, AWS, Google Cloud, enterprise integration, data platforms, DevSecOps, MLOps/LLMOps, or API management.
Experience developing industry-specific AI offerings, reusable solution blueprints and accelerators, pricing or TCO models, competitive positioning, and GTM assets.
Relevant cloud, AI, security, architecture, or data certifications.
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Job Summary
The AI Solution Specialist partners with clients, engineering teams, enterprise architects, sales and alliance stakeholders to shape commercially viable and technically credible OpenAI-enabled solutions for defined pursuits and transformation opportunities. Translates customer challenges, business processes, and desired outcomes into differentiated solution narratives, architecture constructs, implementation scopes, assumptions, commercials, and delivery roadmaps. Acts as the bridge between client ambition and engineering feasibility, ensuring proposals reflect realistic OpenAI capabilities, enterprise integration requirements, AI lifecycle considerations, responsible AI controls, and proven delivery patterns. Apply strong techno-functional knowledge of OpenAI, hyperscale ecosystems, enterprise technology platforms, HCLTech capabilities, and partner co-sell frameworks to create scalable, secure, and outcome-oriented solutions.
Key Responsibilities
Discover and translate client business problems into high-value OpenAI use cases, solution narratives, target outcomes, and measurable success criteria.
Shape pursuit artifacts including the solution vision, logical architecture, implementation scope, assumptions, exclusions, dependencies, risks, operating model, roadmap, and high-level commercial inputs.
Collaborate with engineering and architecture teams to validate application feasibility, model suitability, integration complexity, data readiness, security requirements, and production constraints.
Define an evaluation-led solution approach covering baseline performance, acceptance criteria, quality thresholds, safety testing, user validation, and continuous improvement.
Ensure proposals account for the complete AI solution lifecycle, including discovery, prototyping, evaluation, industrialization, deployment, governance, monitoring, adoption, and optimization.
Align OpenAI solution designs hyperscale environments such as Microsoft Azure, AWS, and Google Cloud, as well as relevant enterprise platforms, data services, security controls, and systems of record.
Incorporate applicable technologies and offerings from strategic OEMs and partners while maintaining clear architectural accountability and avoiding unnecessary solution complexity.
Apply HCLTech reference architectures, delivery playbooks, reusable accelerators, industry assets, and commercial constructs to improve pursuit speed, consistency, differentiation, and win probability.
Facilitate technical alignment and solution-shaping workshops with client stakeholders, delivery leaders, engineers, security teams, alliance partners, and commercial teams.
Develop credible sizing, effort, capacity, consumption, and pricing inputs in collaboration with engineering, delivery, finance, and commercial stakeholders.
Articulate solution differentiation based on business value, implementation feasibility, responsible AI, enterprise governance, time to value, and the ability to scale from pilot to production.
Skill Requirements
The following AI skills are mandatory for this role:
Strong AI and LLM fundamentals, including prompt and context engineering, embeddings, RAG, vector search, agentic workflows, orchestration, model selection, evaluations, and AI lifecycle considerations.
Working knowledge of OpenAI or Azure OpenAI APIs and platform capabilities, ChatGPT Enterprise use cases, OpenAI Codex or comparable AI-assisted engineering tools, and relevant hyperscale AI services.
Ability to identify and prioritize high-value AI use cases through client discovery, process assessment, data-readiness analysis, technical feasibility, value mapping, and measurable business-outcome definition.
Ability to shape end-to-end AI solutions covering target architecture, enterprise integration, delivery scope, effort and consumption estimates, implementation roadmap, operating model, adoption, and pilot-to-production scaling.
Ability to incorporate evaluation criteria, Responsible AI, security, privacy, governance, safety guardrails, human oversight, observability, and operational controls into proposals and solution designs.
Other Requirements
5+ years of experience in presales, GTM solutioning, solution architecture, technology consulting, or digital transformation.
Proven experience leading client discovery and solution-shaping workshops, prioritizing use cases, developing value propositions, and translating business requirements into differentiated and technically credible AI solutions.
Experience developing solution narratives, architecture blueprints, delivery scope, assumptions, dependencies, risks, effort and consumption estimates, implementation roadmaps, commercials, and business cases.
Experience contributing to RFI/RFP responses, proposals, presentations, demonstrations, POCs, pilots, executive briefings, and deal reviews in collaboration with sales, engineering, delivery, finance, and alliance teams.
Strong client-facing communication, storytelling, negotiation, and stakeholder-management skills, supported by a sound understanding of cloud, data, integration, security, governance, and production AI requirements.
Preferred Qualifications
Experience with Azure, AWS, Google Cloud, enterprise integration, data platforms, DevSecOps, MLOps/LLMOps, or API management.
Experience developing industry-specific AI offerings, reusable solution blueprints and accelerators, pricing or TCO models, competitive positioning, and GTM assets.
Relevant cloud, AI, security, architecture, or data certifications.
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