Role: Search Engineer
Industry Type: Advertising & Marketing
Department: Engineering - Software & QA
Employment Type: Full Time, Permanent
Role Category: Software Development
UG: B.Sc in Any Specialization
PG: LLM in Law
Job Role: AI Developer / Full Stack Engineer Location: Bangalore ABOUT UNILEVER With 3. 4 billion people in over 190 countries using our products every day, Unilever is a business that makes a real impact on the world. Work on brands that are loved and improve the lives of our consumers and the communities around us. We are driven by our purpose: to make sustainable living commonplace, and it is our belief that doing business the right way drives superior performance. At the heart of what we do is our people we believe that when our people work with purpose, we will create a better business and a better world. At Unilever, your career will be a unique journey, grounded in our inclusive, collaborative, and flexible working environment. We don t believe in the one size fits all approach and instead we will equip you with the tools you need to shape your own future.
About Us
Wild are on a mission to remove single-use plastic from the bathroom, armed with our refillable, natural and scent-sational deodorants, body wash, and lip balm and we re only just getting started. We launched in 2020 and as a high-growth company we re already one of Europe s fastest growing start-ups. Role Summary: The AI Developer / Full Stack Engineer is the primary builder of Wilds autonomous agents and the applications around them. Where the business today uses LLMs in a chat-based way, this role designs and ships agents that take action calling tools and APIs, executing multi-step workflows, and operating with minimal human intervention. You will work end-to-end: designing agent logic in Google Agent Builder, integrating with Snowflake and business systems, and building the interfaces and services that let users interact with and supervise agents. This is a deeply hands-on engineering role for someone who enjoys turning ambiguous business problems into working, reliable software. Quality and trust are central. You will build with evaluation, observability, and guardrails in mind, ensuring agents behave predictably, fail safely, and keep humans in control where it matters. You will iterate quickly, but always toward production-grade outcomes.
Key Responsibilities
Design, build, and deploy AI agents on Google Agent Builder, including prompts, tools/functions, orchestration, and memory. Develop full-stack applications front-end interfaces and back-end services/APIs that expose and supervise agent capabilities. Integrate agents with Snowflake, internal systems, and third-party APIs to enable real task execution. Implement retrieval (RAG) against Wilds data to ground agent responses and actions. Build evaluation and testing harnesses for agent accuracy, safety, and regression control. Instrument observability logging, tracing, and metrics for agent behaviour and performance. Apply guardrails and human-in-the-loop patterns for sensitive or high-impact actions. Optimise for performance and cost in collaboration with FinOps (model choice, caching, token-efficient design). Support responsible AI practices and governance Implement event-driven and API-based integrations (microservices patterns) Required Skills & Experience 5+ years in software engineering with strong full-stack capability (e. g. Python/TypeScript; React or similar front-end). 3+ years hands-on experience building LLM-powered or agentic applications (tool calling, RAG, orchestration frameworks). Practical experience with GCP and ideally Vertex AI / Google Agent Builder. Strong API design and systems integration skills. Comfort working with data sources such as Snowflake and writing performant SQL. Familiarity with prompt engineering, agent evaluation, and safe-deployment practices. Solid software hygiene: Git, CI/CD, testing, and code review. Understanding of multi-agent systems and distributed architectures Familiarity with LLMOps / GenAIOps concepts Exposure to event-driven and scalable cloud systems Experience in scaling and optimization of LLM systems Experience using AI delivery tools like Claude Code, Antigravity etc Preferred Qualifications Experience with agent frameworks/enterprise AI platforms/ orchestration and vector search/embeddings. Exposure to cloud cost optimisation / FinOps practices GCP associate or professional certification. Prior work shipping AI features to real users in production. Bachelors in Computer Science, Software Engineering, or equivalent practical experience. Key Success Metrics Agents/features shipped to production and adopted by business users. Agent task success rate and reduction in error/escalation rates over time. Cycle time from use-case definition to a deplo