# Solution Architect - Skills & Reusability at Unilever

Unilever is hiring a Solution Architect - Skills & Reusability. Level rates it Works on AI ●●●○; you can [apply on Unilever](https://jobsbylevel.com/go/0ca04a9b-7ac6-4c1f-b651-acaf85ca41e3?utm_medium=ai-agent).

AI Level 3, AI centrality 68 out of 100. Uniops Bangalore Centre.

## Details

- Company: [Unilever](https://jobsbylevel.com/companies/unilever)
- AI level: AI Level 3 (score 68 out of 100)
- Location: Uniops Bangalore Centre
- Posted: November 11, 2026
- Apply: https://jobsbylevel.com/go/0ca04a9b-7ac6-4c1f-b651-acaf85ca41e3?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=solution-architect-skills-reusability-at-unilever-8cc95f

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## Description

Job Title: Solution Architect - Skills & Reusability Work Level: 2B JOB PURPOSE We are looking for a Senior Manager, AI Solution Architecture to provide end-to-end architecture leadership for enterprise artificial intelligence and agentic solutions across technologies and platforms. As part of the Hyper Automation Technical Design Authority, you will make sure solutions are secure, scalable, reusable, cost-efficient and future-fit. You will be accountable for agentic architecture, grounding and retrieval, integration patterns, model selection and benchmarking, evaluation, large language model operations, model drift and token and cost optimisation. This is a technical design authority role rather than a delivery engineering role: you will decide and assure the architecture while partnering with the product and engineering teams that build, deploy and operate solutions. You will work closely with Enterprise Architecture, security, privacy, engineering and platform teams, and with technology vendors, to turn emerging artificial intelligence capabilities into practical enterprise patterns. Because here, we don’t just hire for jobs. We invite you to be part of something bigger. KEY RESPONSIBILITIES Define and govern reference architectures and engineering patterns for artificial intelligence, generative artificial intelligence and agentic solutions. Lead technical design reviews and provide clear design pass, hold or remediation decisions. Design enterprise patterns for agent orchestration, retrieval and grounding, application programming interfaces, tools, skills and enterprise-system integration. Assess artificial intelligence models for task suitability, quality, latency, security, economics and operational fit, and establish benchmarking approaches across model and workload classes. Drive token optimisation and cost-per-outcome engineering, including appropriate use of smaller, lower-cost models and routing strategies. Define evaluation, observability, large language model operations (LLMOps) and model-drift patterns, including criteria to hold, roll back or promote solutions. Determine when model tuning is justified versus prompting, retrieval, tool and skill design or model selection. Build proofs of value for emerging technologies and convert successful findings into reusable enterprise patterns. Identify opportunities for reuse before new engineering is commissioned. Work across platform boundaries, including cloud artificial intelligence platforms, agent frameworks and deterministic automation technologies. Partner with Enterprise Architecture, security, privacy and engineering teams to make sure designs meet enterprise guardrails. Help mature solutions along the autonomy ladder, introducing appropriate human-in-the-loop, self-monitoring and automated remediation patterns. REQUIRED QUALIFICATIONS Strong enterprise or solution architecture experience, with hands-on depth in generative and agentic artificial intelligence architecture. Proven experience designing production-grade agentic systems, including orchestration, tool and skill invocation, multi-agent patterns and human-in-the-loop controls. Strong grounding in large language models and model architecture, including model selection, benchmarking, context and token management and cost optimisation. Deep understanding of retrieval-augmented generation, enterprise search, embeddings, vector retrieval, grounding and knowledge integration. Strong knowledge of agent integration patterns, including application programming interfaces, event-driven integration and Model Context Protocol (MCP) style tool integration. Experience designing evaluation frameworks covering quality, groundedness, task completion, latency, safety and cost. Experience with large language model operations (LLMOps) or generative artificial intelligence operations, observability, model drift and production monitoring. Strong cloud architecture experience with at least one major hyperscaler, such as

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