Role & responsibilities

About the Role The AI Solutions Architect - Delivery (internally designated AI Platform and Solutions Innovator, Grade 3A) owns the end-to-end delivery of AI and GenAI engagements, from shaping and estimating the solution, through agile execution, to production deployment and steady-state support. This is a delivery and implementation role rather than a pure strategy role.

The person in this role is the anchor point between Engineering, Solution Architecture, QA, BA, Account Management, and the client's project and management teams. They set the technical direction, choose the right AI tech stack and LLMs for the problem at hand, build MVPs and POCs to prove the approach, and keep guardrails, security, privacy, and compliance embedded throughout, without letting delivery timelines or quality slip.

A hands-on AI development background is not mandatory. What matters is the ability to guide, mentor, and make sound technical and delivery decisions that keep multiple AI projects moving toward measurable business outcomes.

Key Responsibilities

- Requirement Analysis & Solution Estimation

Understand client requirements in depth and produce high-level solution estimates covering functional, non-functional, scale, performance, and cost-optimisation aspects.
- Rapid MVP / POC Development & Demonstration

Build MVPs and POCs using AI tools to validate solution approaches and demonstrate outcomes persuasively to client and leadership audiences.
- Multi-Project Delivery Execution

Drive execution across multiple concurrent AI projects using agile methodology, ensuring every engagement delivers against defined business outcomes.
- AI Product Lifecycle Ownership

Guide solutions through the full AI product lifecycle - ideation, prototyping, build, testing, production deployment, and ongoing evolution.
- Client Communication & Delivery Governance

Own client communication throughout the engagement, tracking timelines, quality, and continuous delivery against agreed milestones.
- LLM & AI Tech Stack Selection

Select the most appropriate LLMs and AI tech stack for each solution, balancing quality of outcome against cost and resource utilisation.
- Guardrails, Security, Privacy & Compliance

Embed guardrails, security, privacy, and compliance controls into every solution without compromising delivery timelines or quality.
- Production Readiness & Support Framework

Apply AIOps, DevOps,



and production-environment expertise to ensure solutions are deployable, observable, and supportable, and establish the support framework for steady-state operations.
- Reusable Frameworks, Accelerators & Automation

Create reusable frameworks and accelerators that reduce implementation time, applying an automation-first mindset from design through development to production deployment.
- Technical Mentorship, Risk & Resourcing

Anchor the engagement across Engineers, QA, BA, Account Managers, and client teams - guiding architecture and GenAI solutioning, mentoring Engineers and Solution Architects, calling out risks early with mitigation plans, and ensuring the right resource is in the right place. Required Experience & Qualifications

- Bachelor's degree in Computer Science, Engineering, Data Science, or a related field; a relevant master's degree or professional certification is an advantage.
- 5-8 years of experience in AI/technology project delivery, solution implementation, or technical project management, including hands-on exposure to GenAI or AI/ML-based solutions.
- Demonstrated experience translating client requirements into high-level solution estimates covering functional, non-functional, scale, performance, and cost dimensions.
- Proven track record of running projects under agile methodology (Scrum/Kanban), including sprint planning, backlog management, and delivery tracking.
- Working knowledge of AIOps, DevOps, CI/CD pipelines, and production environments.
- Experience building MVPs and POCs using AI tools and presenting them to client or leadership audiences.
- Practical experience selecting and evaluating LLMs and AI platforms against capability, performance, and cost trade-offs.
- Familiarity with AI guardrails, security, data privacy, and compliance practices applicable to production AI/GenAI solutions.
- Strong client-facing communication and stakeholder management experience across technical and business audiences.
- Agile or project management certification (CSM, PMI-ACP, SAFe, PMP) is preferred.





Key Skills & Competencies

- Solution estimation and sizing across functional, non-functional, scale, performance, and cost dimensions.
- Rapid prototyping with AI/GenAI tooling, paired with strong demonstration and storytelling ability for senior audiences.
- Agile delivery management (Scrum/Kanban) across multiple parallel workstreams, held to measurable business outcomes.
- End-to-end understanding of the AI product lifecycle, from experimentation through productisation to steady-state operations.
- Client-facing communication and stakeholder management across technical and business audiences, with disciplined delivery tracking.
- Sound judgement in evaluating LLMs and AI platforms on capability, latency, scalability, and total cost of ownership.
- Working knowledge of responsible-AI guardrails, data privacy, security practices, and compliance requirements for production AI.
- Practical fluency in AIOps, DevOps, CI/CD, and production environments, with the ability to design sustainable support models.
- Automation-first mindset with demonstrated experience building reusable assets, accelerators, and repeatable delivery patterns.
- Mentoring and cross-functional leadership without being the primary hands-on builder.
- Proactive risk identification with mitigation planning, and effective resource allocation across engagements.

Ideal Candidature The ideal candidate is a delivery-minded technologist who is equally comfortable in an architecture discussion, a client steering committee, and a sprint review. They think in terms of business outcomes rather than tasks, and they instinctively look for the reusable pattern rather than solving the same problem twice.

- Bridges the technical and business worlds, explaining an LLM trade-off to an engineer and a delivery risk to a client executive with equal clarity.
- Leads through influence and mentorship rather than hands-on coding, lifting the capability of Engineers and Solution Architects around them.
- Brings an automation-first, accelerator-driven mindset that compounds delivery speed across engagements.
- Treats guardrails, security, privacy, and compliance as design inputs rather than afterthoughts.
- Surfaces risks early with a mitigation plan already in hand, rather than escalating problems late.
- Thrives in a multi-project setting and stays structured and composed under delivery pressure.

📌 AI Solutions Architect - Delivery (Pune)
🏢 Fulcrum Worldwide Software
📍 Pune