About Us
We love going to work and think you should too. Our team is dedicated to trust, customer obsession, agility, and striving to be better everyday. These values serve as the foundation of our culture, guiding our actions and driving us towards excellence. We foster a culture of performance and recognition, allowing us to transform growth as we enable our employees to do the best work of their careers.
This position is located in Pune, India. You'll be working in a major tech center. Across the globe, our Centers of Energy serve as hubs where we accelerate productivity and collaboration, inspire creativity, and cultivate a culture of connection and celebration. Our teams coordinate their time in Centers of Energy to reflect how they work best.
What You'll Do
LogicMonitor® is the AI-first hybrid observability platform powering the next generation of digital infrastructure. LogicMonitor delivers complete visibility and actionable intelligence across on-premises, cloud, and edge environments. By anticipating issues before they strike, optimizing resources in real time, and enabling faster, smarter decisions, LogicMonitor helps IT and business leaders protect margins, accelerate innovation, and deliver exceptional digital experiences without compromise.
LogicMonitor is advancing observability through AI‑driven data intelligence, connecting massive telemetry streams with the reasoning capabilities of large language models. We’re looking for a Software Engineer who sits at the intersection of backend systems and data engineering, capable of building scalable data pipelines, APIs, and retrieval frameworks that fuel Edwin AI, Dexda, and other AIOps products. You’ll design, build, and optimize the data infrastructure that makes GenAI‑powered insights reliable, explainable, and real‑time.
- Design and build streaming and batch data pipelines that process metrics, logs, and events for AI workflows.
- Develop ETL and feature‑extraction pipelines using Python and Java microservices.
- Integrate data ingestion and enrichment from multiple observability sources into AI‑ready formats.
- Build resilient data orchestration using Kafka, Airflow, and Redis Streams.
- Implement retrieval‑augmented generation (RAG) pipelines with vector databases (Milvus, Qdrant, OpenSearch, Neo4j Vector).
- Develop data indexing and semantic search for large‑scale observability and operational data.
- Work with structured and unstructured data lakes and warehouses (Delta Lake, Iceberg, ClickHouse).
- Collaborate with the AI Platform team to manage embeddings, metadata, and model context storage.
- Optimize latency and throughput for retrieval, query expansion, and AI response generation.
- Build and maintain Java microservices (Spring Boot) that serve AI and analytics data to Edwin and AIOps applications.
- Develop Python APIs (FastAPI / LangGraph) for LLM orchestration, summarization, and correlation reasoning.
- Implement schema contracts and streaming protocols (REST, gRPC, SSE, WebSockets) between services.
- Ensure fault‑tolerant, observable, and performant API infrastructure.
- Instrument services with OpenTelemetry for unified metrics, tracing, and logging.
- Implement data validation, schema evolution, and lineage tracking across AI pipelines.
- Enforce data privacy, RBAC, and compliance for model inputs and stored context.
- Collaborate with SRE and AI teams to monitor and optimize end‑to‑end AI system performance.
What You'll Need
- Bachelor’s degree in Computer Science, Data Engineering, or a related field.
- 3+ years of experience in backend or data systems engineering.
- Experience building streaming data pipelines (Kafka / Spark or any similar technology).
- Strong programming background in Java and Python, including microservice design.
- Experience with ETL, data modeling, and distributed storage systems.
- Familiarity with LLM pipelines, embeddings, and vector retrieval.
- Understanding of Kubernetes, containerization, and CI/CD workflows.
- Awareness of data governance, validation, and lineage best practices.
- Strong communication and collaboration across AI, Data, and Platform teams.