Engineer II, Full Stack Engineer

Overview

Full Stack Engineer II in Enterprise AI at GSK, building and shipping AI-integrated features independently, working closely with GSK business teams to prototype against real data and workflows at a days-to-weeks cadence.

What you'll do

• Own use cases from problem framing through working prototype and first production version, without close supervision.
• Qualify incoming asks by challenging underspecified problems, assessing feasibility and defining scope before committing to work.
• At the end of each piece of work, form and defend an evidence-based recommendation to continue or stop, presented to business and EAI leadership.
• Work closely with business teams, prototyping against real data and workflows and building trusted relationships with product owners and engineering leads.
• Build with digital workers as the default mode of engineering: direct them, validate what they generate and course-correct at engineering speed.
• Review, test and approve digital worker output within the remit EAI's operating model sets, and escalate what falls outside it.
• Design and orchestrate multi-agent workflows end-to-end, and judge when agentic approaches add value over simpler alternatives.
• 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.
• 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.
• Raise Responsible AI and governance considerations from the prototype stage.
• Deliver complete UI features with measurable user engagement, bringing user feedback directly into build decisions.
• Build AI-powered interfaces with confidence signals, error states, provenance and human-override paths.
• 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.
• Build reusable components, SDKs, agents, prompt templates and reference architectures adopted by engineers outside your 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.
• Hand work on cleanly when it moves to another team, with working code, architecture rationale, open risks and scaling concerns.

What you'll need

• 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.
• 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.
• Bachelor's degree in Computer Science, Data Science, Statistics, Applied Mathematics or a related field, or equivalent practical experience.

Nice to have

• A startup, lab or innovation-team background.
• Prior experience as a Full Stack Engineer, Customer Engineer or Solutions Architect, ideally at an AI-native company or hyperscaler.
• Experience in regulated industries, such as life sciences, healthcare or financial services, and familiarity with GxP, GDPR and SOX.
• Working knowledge of fine-tuning fundamentals and Model Context Protocol (MCP) integrations.

Details

• Location: Bengaluru Luxor North Tower.