22 hours ago
Bengaluru, IndiaSenior
Responsibilities
- Provide solution architecture and technical design assurance for AI and agentic use cases across multiple technologies and platforms.
- Translate business requirements into architectures covering agents, models, skills, tools, data, integration and orchestration.
- Conduct technical design reviews and ensure solutions meet architecture and engineering standards before build and production.
- Define reusable patterns for orchestration, grounding, agent-to-agent interaction, human-in-the-loop controls, APIs, MCP, tool integration and deterministic automation.
- Assess solutions for scalability, resilience, observability, maintainability, security, privacy, governance and cost.
- Optimise token consumption, model selection, latency, model utilisation and runtime cost across agentic workloads.
- Maintain and govern the enterprise Agent Catalogue and Skill Registry, including asset metadata, ownership, dependencies, versioning and lifecycle status.
- Identify duplicate assets and convert recurring solution requirements into reusable architecture and engineering patterns.
- Create architecture decision records, technical design documents and reusable reference patterns.
- Partner with product, engineering, platform, security, Enterprise Architecture and business stakeholders to industrialise successful proofs of value.
Requirements
- Strong solution architecture experience covering AI, generative AI, automation or modern cloud applications.
- Hands-on experience designing agentic or generative AI solutions beyond the proof-of-concept stage.
- Understanding of agent orchestration, tool and skill invocation, multi-agent patterns and human-in-the-loop architecture.
- Strong knowledge of large language models, context windows, prompting, token consumption, model selection, retrieval-augmented generation, grounding, vector search and enterprise knowledge integration.
- Strong API, integration and event-driven architecture knowledge, including emerging standards such as MCP and reusable tool and skill architectures.
- Cloud architecture experience with Microsoft Azure or Google Cloud Platform.
- Ability to review designs for scale, security, resilience, observability and maintainability and translate governance requirements into technical controls.
- Experience optimising generative AI solutions for tokens, latency, model utilisation and runtime cost, including model routing and workload right-sizing.
- Experience with reusable component libraries, service or API catalogues, agent or skill registries, or equivalent enterprise asset management.
- Ability to identify duplication and convert solution-specific components into reusable patterns, while communicating technical decisions clearly and constructively.
- Preferred qualifications include Microsoft Copilot Studio, MCP servers, plugins, connectors, LLMOps, AI observability, model evaluation, prompt and context engineering, AI FinOps, knowledge graphs, semantic search, DevOps, developer portals and internal technology marketplaces experience.
- Experience in fast-moving consumer goods, retail, eCommerce or global manufacturing is preferred.
Benefits
- Unilever is committed to inclusion, equal opportunity and supporting employee growth and development.
Tech Stack
Categories
Solutions Engineering
About Unilever
Unilever is a public consumer-goods manufacturer that develops and markets branded food, home-care, and personal-care products for mass-market consumers worldwide. Its portfolio includes brands such as Dove, Rexona, Domestos, Hellmann’s, Knorr, and Ben & Jerry’s, sold through retailers and e-commerce. Founded in 1872 and headquartered in London’s Blackfriars, it operates across about 190 countries and is listed on the London Stock Exchange.