Job Description 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. Qualifications Basic Qualifications · Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience. · 5–8 years of software engineering experience building, architecting, and operating large-scale production backend systems. · Language & Stack Mastery: Deep expertise in either Python/Java, with a command of modern ecosystems: If Python: Proficiency in FastAPI / Asyncio, type hinting (Pydantic), and managing high-concurrency event loops. If Java: Proficiency in Spring Boot / Quarkus, Reactive programming (Project Reactor/Vert.x), and JVM performance tuning. · API & Distributed Systems: Proven experience designing and implementing high-performance REST and gRPC APIs, with understanding of distributed system patterns (e.g., circuit breakers, service discovery, and eventual consistency). · Full Ownership: A track record of owning and delivering complex systems end-to-end—from initial scoping and architectural design to implementation, automated testing, and production operations (SRE mindset). · Strategic Problem Solving: Demonstrated ability to independently identify and frame engineering problems. You should be able to navigate ambiguous or incomplete signals to define technical roadmaps rather than just executing against a pre-defined ticket. · AI Implementation: Hands-on experience integrating LLM APIs (OpenAI, Anthropic, or open-source models) into production workflows, including prompt engineering and managing the non-deterministic nature of AI outputs. Preferred Qualifications: · Experience designing or leading the development of AI agent systems — tool use, multi-agent coordination, memory, and autonomous task execution. · Command of AI-native SDLC practices: eval-driven development, prompt versioning and regression testing, agent observability/tracing. · Experience with workflow orchestration and durable execution frameworks (e.g., Temporal, Airflow) for reliable multi-step agentic pipelines. · Track record of establishing engineering standards — testing patterns, observability practices, deployment conventions — that a team adopts and builds on. · Experience integrating deeply with enterprise SaaS platforms (Slack, Jira, Confluence, ServiceNow, etc.) and designing resilient integration layers. Technologies You'll Work With: · Proficiency in Python or Java (Open to either, with a willingness to work across the stack). · Experience with LLM APIs (OpenAI, Anthropic, etc.), Vector Databases (Pinecone, Weaviate, Milvus), and Orchestration Patterns (Chains, Agents, Tool-calling). · Building high-performance services using FastAPI/Flask (Python) or Spring Boot/Quarkus (Java); experience with gRPC and RESTful API design. · Distributed systems experience using Message Queues (Kafka, SQS, or RabbitMQ) to handle long-running agentic tasks. · Relational databases (PostgreSQL/MySQL), Object Storage (S3), and understanding of data modeling for RAG (Retrieval-Augmented Generation).

About LinkedIn

Founded in 2003, LinkedIn connects the world's professionals to make them more productive and successful. With more than 1 billion members worldwide, including executives from every Fortune 500 company, LinkedIn is the world's largest professional network. The company has a diversified business model with revenue coming from Talent Solutions, Marketing Solutions, Sales Solutions and Premium Subscriptions products. Headquartered in Silicon Valley, LinkedIn has offices across the globe.