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SeniorSoftwareEngineer-ProductivityEngineering
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“Senior Software Engineer - Productivity Engineering at LinkedIn. Skills: AI agent systems, LLM integration, System design, Production operations. Investigate employee productivity pain points. Synthesize signals from data”
What You'll Achieve.
Raise technical bar; Replace manual support experiences; Put AI at center; Build, evaluate, operate systems at scale; Ship systems; Own problem space; Translate ambiguous signals; Build with rigor; Ensure systems are debuggable; Ensure systems are auditable; Ensure systems are continuously improvable; Balance expressiveness with guardrails; Raise floor for IC2s
Industry & Context.
Strategic problem solving; Troubleshooting
What They're Looking For.
Must Have
Bachelor's degree in Computer Science or related technical field, or equivalent practical experience, 5–8 years of software engineering experience, Deep expertise in Python/Java, Proficiency in FastAPI / Asyncio, Pydantic, and high-concurrency event loops, Proficiency in Spring Boot / Quarkus, Reactive programming, and JVM performance tuning, Proven experience designing and implementing high-performance REST and gRPC APIs, Understanding of distributed system patterns, Track record of owning and delivering complex systems end-to-end, SRE mindset, Demonstrated ability to independently identify and frame engineering problems, Hands-on experience integrating LLM APIs
Nice to Have
Experience designing or leading AI agent systems, Command of AI-native SDLC practices, Experience with workflow orchestration and durable execution frameworks, Track record of establishing engineering standards, Experience integrating deeply with enterprise SaaS platforms
What You'll Do.
Investigate employee productivity pain points
Synthesize signals from data
Derive solution context
Translate problem contexts into technical designs
Make principled tradeoffs
Architect AI agentic systems
Deliver Python-based services
Orchestrate LLM-powered agents
Define AI-native engineering approach
Define eval-driven development
Define agent observability
Define prompt lifecycle management
Define safe autonomous action
Own standards for testing
Own standards for tracing
Own standards for monitoring
Design tool ecosystem for agents
Interact with internal platforms
Provide technical guidance
Conduct design reviews
Mentor IC2s on AI systems
Partner with product teams
Partner with operations teams
Partner with platform teams
Identify automation opportunities
Shape engineering roadmap
How You'll Work.
Team & Collaboration
Cross-functional roadmap; Partner with product; Partner with operations; Partner with platform
Communication Scope
Technical direction; Measurable outcomes
Process & Methodology
Roadmap planning
Full Job Description
LinkedIn was built to help professionals achieve more in their careers, and every day millions of people use our products to make connections, discover opportunities and gain insights. Our global reach means we get to make a direct impact on the world’s workforce in ways no other company can. We’re much more than a digital resume – we transform lives through innovative products and technology. 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 principle
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