Software Engineer - Productivity Engineering

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 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 experiences. You will build end-to-end systems that put AI agents at the center of the employee productivity loop — from request intake to resolution — dramatically reducing toil and enabling people to focus on high-leverage work.

Responsibilities

• Build AI agentic systems end-to-end — design, implement, and ship services that orchestrate LLM-powered agents to handle employee support workflows autonomously.
• Own the full development lifecycle — from requirements and architecture through implementation, testing, deployment, and monitoring, following AI-native SDLC practices.
• Develop agent tooling and integrations — build the tools and connectors agents use to interact with internal systems (ticketing, knowledge bases, identity, SaaS platforms) in a safe, auditable way.
• Instrument for observability — define evals, trace agent reasoning chains, and build feedback loops so system quality is measurable and continuously improvable.
• Iterate rapidly with data — use production signals, user feedback, and eval frameworks to drive model prompt tuning, retrieval improvements, and workflow refinements.
• Collaborate cross-functionally — partner with product, operations, and platform teams to translate employee pain points into automated solutions with measurable impact.
• Write high-quality, maintainable code — enforce engineering standards through code reviews, testing, and documentation that enables the team to move fast with confidence.
  
  

Basic qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
  
  

• 2+ years of software engineering experience building, testing, and shipping production-grade backend systems.
  
  

• Professional experience programming in Python or Java.
  
  

  • If Python: Experience with FastAPI, Flask, or Django, including experience with async/await and type hinting.
  • If Java: Experience with Spring Boot or equivalent frameworks including concurrency and JVM fundamentals.

• Hands-on experience developing and maintaining REST or gRPC APIs, with a focus on building scalable, maintainable service logic and background integrating with enterprise systems via APIs or webhooks.
  
  

• Experience building systems end-to-end, including data modeling (SQL/NoSQL), integration with external services, deployment pipelines, and basic production monitoring.
  
  

• Experience delivering features by breaking down technical requirements into actionable code.
  
  
  
  

Preferred qualifications

• Experience building or working with AI agent frameworks.
  
  

• Familiarity with AI-native SDLC practices: prompt versioning, eval-driven development, and agent observability.
  
  

• Exposure to workflow orchestration tools (e.g., Temporal, Airflow) for managing multi-step agentic pipelines.
  
  
  
  

Suggested skills

• Python or Java
  
  

• REST/gRPC API development
  
  

• Databases (PostgreSQL/MySQL; NoSQL)
  
  

• Messaging systems (Kafka/SQS/RabbitMQ)
  
  

• LLMs and vector databases
  
  
  
  

Technologies you’ll work with

• Language: Python or Java, with willingness to work across the stack.
  
  

• AI/ML: LLM APIs (e.g., OpenAI, Anthropic), vector databases (e.g., Pinecone, Weaviate, Milvus), and orchestration patterns (chains, agents, tool-calling).
  
  

• Backend & API: FastAPI/Flask (Python) or Spring Boot/Quarkus (Java); gRPC and RESTful API design.
  
  

• Messaging & async: Distributed systems using message queues (Kafka, SQS, or RabbitMQ) to handle long-running agentic tasks.
  
  

• Data: Relational databases (PostgreSQL/MySQL), object storage (S3), and data modeling for RAG (Retrieval-Augmented Generation).
  
  
  
  

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