Senior Software Engineer - Productivity Engineering at LinkedIn3

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. We are looking for a Senior Software Engineer to join the Productivity Engineering team in Bangalore, where our mission is to transform how employees work by replacing manual, fragmented support experiences with intelligent, AI-native automation. You will lead the design and delivery of end-to-end agentic systems that put AI at the center of the employee productivity loop — and raise the technical bar for how the team builds, evaluates, and operates these systems at scale. This is a high-ownership, high-scope role. Beyond shipping systems, you will own the problem space: independently investigating employee pain points, deriving solution contexts, and translating ambiguous signals into engineering initiatives with clear technical direction and measurable outcomes. You will set standards for how the team approaches AI-native development and mentor others to build with the same rigor. Responsibilities: · Own problem statements end-to-end — proactively investigate employee productivity pain points, synthesize signals from data, stakeholders, and operations, and derive the solution context that frames what we build and why. · Drive solution architecture — translate well-understood problem contexts into technical designs for agentic systems, making principled tradeoffs across reliability, latency, safety, and maintainability. · Architect and deliver AI agentic systems — lead the end-to-end design and delivery of Python-based services that orchestrate LLM-powered agents across complex, multi-step employee support workflows. · Set the technical direction for AI-native engineering — define how the team approaches eval-driven development, agent observability, prompt lifecycle management, and safe autonomous action at scale. · Drive system reliability and quality — own the standards for testing, tracing, and monitoring agentic pipelines so production systems are debuggable, auditable, and continuously improvable. · Expand the agent capability surface — design the tool ecosystem that agents use to interact with internal platforms (ticketing, identity, knowledge, SaaS), balancing expressiveness with guardrails. · Mentor and grow the team — provide technical guidance through code reviews, design reviews, and pair programming; raise the floor for how IC2s approach AI systems work. · Influence cross-functional roadmap — partner with product, operations, and platform teams to identify the highest-leverage automation opportunities and shape the engineering roadmap around them.