AI Solution Architect – Generative AI & Sprinklr
Altraize
Location
🇮🇳 Hyderabad, India
Type
full_time
Salary
Undisclosed
Posted
5d ago
Job Description
We are looking for an experienced AI Solution Architect with strong expertise in Generative AI, LLMs, conversational AI, and Sprinklr-based contact center solutions.
The role
will be responsible for owning the AI solution architecture, translating business outcomes and use cases into technical AI
requirements
, mapping appropriate Sprinklr and Generative AI capabilities, defining scalable solution patterns and guardrails, and providing architecture leadership throughout implementation and delivery. The ideal candidate should remain hands-on with AI architecture and solution design while guiding engineering teams and ensuring production-ready solutions.
Key Responsibilities
- Engage with business and product stakeholders to understand business use cases, customer journeys, constraints, and success criteria.
- Translate business
requirements
into clear and actionable technical AI
requirements
. • Assess and map relevant Sprinklr AI, Conversational AI, Knowledge, Automation, and Integration capabilities to business
requirements
. • Design end-to-end Generative AI solutions covering: • LLMs and prompt engineering • Grounding and RAG • Agentic AI patterns • Orchestration • Enterprise integrations • Context management • Observability • Human handoff • Create solution designs and provide technical guidance to AI, bot, and engineering teams. • Define and review approaches related to intents, entities, dialogues, prompts, knowledge, APIs, integrations, and testing. • Lead technical architecture reviews and resolve design decisions, dependencies, and technical trade-offs. • Ensure AI solutions align with enterprise architecture, integration, data, security, and governance standards. • Review implementation quality, testing results, observability evidence, risks, and production readiness. • Guide teams in defect resolution, performance tuning, optimization, and solution improvements. • Lead backlog refinement and grooming from a technical AI architecture perspective. • Define reusable AI architecture patterns, quality gates, guardrails, risks, and architecture decisions. • Collaborate with cross-functional teams to ensure successful implementation and delivery.