# Full Stack Engineer II **Company**: GSK **Location**: Bengaluru **Experience**: senior **Job type**: full-time **Category**: Engineering **Industry**: Healthcare **Apply**: https://gsk.wd5.myworkdayjobs.com/en-US/GSKCareers/job/Bengaluru-Luxor-North-Tower/Engineer-II--Full-Stack-Engineer_450244-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_1c3e0ada-a81 ## Description Enterprise AI (EAI) builds AI products that combine real modelling work with user-facing applications and ships them to production. Full Stack Engineers move across data, models, platforms, APIs and interfaces in a single workflow. The Full Stack Engineer II independently builds and ships AI-integrated features, working closely with GSK business teams to prototype against real data and workflows at a days-to-weeks cadence. **Key Responsibilities:** - Delivery & Experimentation: - Own use cases from problem framing through working prototype and first production version, at a days-to-weeks cadence and without close supervision. - Qualify incoming asks: challenge underspecified problems, assess feasibility and define scope before committing to work. - Work closely with business teams, prototyping against real data and workflows and building trusted relationships with product owners and engineering leads. - Agent-Operated Engineering: - Build with digital workers as the default mode of engineering: direct them, validate what they generate and course-correct at engineering speed. - Design and orchestrate multi-agent workflows end-to-end, and judge when agentic approaches add value over simpler alternatives. - Platforms & APIs: - Design and ship backend services independently with observability, clear contracts and compliance with non-functional requirements. - Choose API design patterns that suit the consumer and iterate on them based on real integration feedback. - AI Integration & Context: - Build AI-integrated features independently, including RAG pipelines, tool use and multi-step LLM workflows that draw on the governed context layer with the right permissions and provenance. - Test AI-powered features with structured evaluation frameworks before and after release. - AI-Native Experience: - Deliver complete UI features with measurable user engagement, bringing user feedback directly into build decisions. - Build AI-powered interfaces with appropriate transparency: confidence signals, error states, provenance and human-override paths. - LLMOps & System Health: - Build evaluation pipelines and LLM-specific monitoring for your features, including output quality signals, model drift and guardrail compliance. - Respond to AI production incidents with structured diagnosis, and see them through to a fix. - Standards & Reusable Components: - Build reusable components, SDKs, agents, prompt templates and reference architectures adopted by engineers outside your own pod, and drive consolidation where you find duplication. - Raise team standards through code review, documentation and direct feedback, and resolve systemic quality issues in the codebase. **Required Experience:** - 10+ years of full-stack or AI/ML engineering experience, including production deployments. - Demonstrated ability to take ideas from sketch to working software at speed, independently and under time pressure. - Strong proficiency in Python and at least one of TypeScript, JavaScript, Go or similar, with experience designing and shipping backend services and complete UI features. - Hands-on experience with GenAI patterns: agents, multi-agent orchestration, RAG, tool use, prompt engineering and evaluation, including evaluation pipelines and LLM-specific monitoring in production. - Daily use of AI coding agents as the primary mode of development, with sound judgement on what to accept, change or reject. - A track record of raising team standards through code review and documentation, and of contributing reusable engineering patterns that others adopted. ## Skills ### Required - Python - TypeScript - JavaScript - Go - GenAI - RAG - LLMOps - Machine Learning - Deep Learning - Full Stack Development ### Nice to have - Prior experience as a Full Stack Engineer, Customer Engineer or Solutions Architect - Experience in regulated industries, such as life sciences, healthcare or financial services - Working knowledge of fine-tuning fundamentals and Model Context Protocol (MCP) integrations --- Source: [Apply at gsk.wd5.myworkdayjobs.com](https://gsk.wd5.myworkdayjobs.com/en-US/GSKCareers/job/Bengaluru-Luxor-North-Tower/Engineer-II--Full-Stack-Engineer_450244-1?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)