Solutions Architect- AI

USEReady is a global analytics and data management solutions company that helps enterprises harness the power of data to drive smarter decisions. With a strong presence across North America, USEReady partners with Fortune 500 companies to deliver end-to-end analytics, BI, data engineering, and AI/ML solutions. Our Bangalore office serves as a key hub for our finance, technology, and delivery operations.

Experience: 12+ years

Focus: GenAI and agentic solutions presales

About the Role

Own technical solutioning for AI and agentic pursuits. Qualify what is real, kill what is not, and design systems that survive contact with enterprise constraints.

Key Responsibilities

• Lead AI discovery workshops — separating use cases with genuine ROI from ones that sound impressive and will not clear procurement.

• Design GenAI and agentic solutions end to end: retrieval architecture, agent orchestration, tool and MCP integration, human-in-the-loop design, and evaluation strategy.

• Define accuracy and performance targets in proposals, and define how they will be measured. Ambiguous success criteria are the single biggest source of fixed-price pain.

• Own the technical response for AI pursuits: architecture, estimates, model and platform selection, and total cost of ownership.

• Build demos and PoCs against client-representative data, including synthetic data approaches where client environments are not available.

• Address enterprise concerns directly — data residency, PII handling, model governance, auditability, and alignment to NIST AI RMF or ISO 42001 where clients require it.

• Advise clients on where agents genuinely outperform deterministic pipelines, and where they do not.

Required Skills & Experience

• 12+ years in software, data, or ML, with 3+ years building or architecting production LLM-based systems.

• Practical depth in RAG architectures, agent frameworks, prompt and context engineering, and evaluation methodology — you know why systems fail in production, not just how to demo them.

• Familiarity with the major model providers and the tradeoffs between them on cost, latency, and reasoning quality.

• Grounding in the underlying data layer — able to tell when an AI use case is really a data readiness problem.

• Credible with a skeptical CIO and a security review board, not just an innovation team.

• Strong analytical and problem-solving skills, with the ability to translate business needs into technical solutions.

Nice to Have

• Oil & Gas, manufacturing, or financial services domain experience.

• Exposure to AI governance frameworks (NIST AI RMF, ISO 42001, EU AI Act).

• Experience in a consulting or systems-integrator environment.