22 hours ago
Bengaluru, IndiaStaff+
Responsibilities
- Define and govern reference architectures and engineering patterns for AI, generative AI, and agentic solutions.
- Lead technical design reviews and issue design pass, hold, or remediation decisions.
- Design patterns for agent orchestration, retrieval, grounding, APIs, tools, skills, and enterprise-system integration.
- Assess and benchmark models for quality, latency, security, economics, and operational fit.
- Drive token optimization, model routing, and cost-per-outcome engineering.
- Define evaluation, observability, LLMOps, model-drift, monitoring, rollback, and promotion patterns.
- Build proofs of value and convert successful emerging-technology findings into reusable enterprise patterns.
- Partner with architecture, security, privacy, engineering, platform, and technology-vendor teams to enforce enterprise guardrails.
- Design human-in-the-loop, self-monitoring, automated-remediation, and higher-autonomy patterns.
Requirements
- Strong enterprise or solution architecture experience with hands-on generative and agentic AI architecture depth.
- Proven experience designing production-grade agentic systems with orchestration, tool and skill invocation, multi-agent patterns, and human-in-the-loop controls.
- Strong knowledge of large language models, model selection, benchmarking, context and token management, and cost optimization.
- Deep understanding of retrieval-augmented generation, enterprise search, embeddings, vector retrieval, grounding, and knowledge integration.
- Experience with API, event-driven, and Model Context Protocol-style integrations and with evaluation frameworks covering quality, groundedness, task completion, latency, safety, and cost.
- Experience with LLMOps or generative AI operations, observability, model drift, and production monitoring.
- Strong cloud architecture experience with at least one major hyperscaler, such as Microsoft Azure or Google Cloud Platform.
- Understanding of security and Responsible AI architecture, including identity, access control, data boundaries, auditability, and guardrails.
- Ability to create technical design documents, reference architectures, architecture decision records, and engineering standards.
- Hands-on experience with enterprise AI ecosystems including Microsoft Azure AI, Azure OpenAI, Microsoft Foundry, Google Cloud Vertex AI, Gemini, Anthropic Claude, and Microsoft Copilot Studio.
- Experience with agent orchestration frameworks such as Agent Development Kit, Semantic Kernel, or equivalent.
- Preferred experience includes RPA, hyperautomation, prompt and context engineering, AI FinOps, automated AI testing, CI/CD for prompts and evaluations, self-healing automation, reusable component catalogues, and platform engineering.
- Experience in fast-moving consumer goods, retail, eCommerce, or global manufacturing is preferred.
Benefits
- Opportunity to lead the technical design authority for enterprise AI and agentic solutions at global scale.
- Opportunity to set reference architectures and standards, lead model benchmarking across providers, develop reusable enterprise patterns, and advance solutions toward greater autonomy.
- Unilever states that it supports inclusion, equal opportunity, and employee growth and development.
Tech Stack
Categories
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.