Senior Backend Software Engineer (ObservoAI)

Join ObservoAI as a Senior Backend Software Engineer, where you will lead the architectural design and technical strategy for high-performance systems that process massive volumes of telemetry data. You will drive technical excellence, lead complex engineering initiatives, and provide mentorship to junior and senior engineers. This role offers a range of benefits, including unlimited time off, flexible spending accounts, and a 401K plan.

• Lead the architectural design and technical strategy for high-performance systems that process massive volumes of telemetry data.
• Drive the development and optimization of ML-driven data routing and transformation engines to reduce customer data volumes significantly.
• Design cloud-native microservices and APIs that integrate seamlessly with major observability platforms while establishing robust monitoring and observability solutions.
• Those who thrive here actively seek out new solutions, experiment thoughtfully, and apply what they learn to drive better, faster, smarter outcomes
• We’re looking for people who are relentlessly curious and committed to continuous learning
• Strong leadership and technical communication skills, with a track record of mentoring engineers, planning technical strategy, and driving decisions across multiple teams and stakeholders
• 5+ years of software engineering experience focused on distributed systems, data engineering, or ML infrastructure with expert-level proficiency in Go, Rust, or Java
• Extensive experience with cloud platforms (AWS, GCP, Azure), container orchestration (Kubernetes), and a proven track record leading and scaling data pipelines using Kafka, Spark, or Flink
• Expert knowledge of observability tools and standards, including Prometheus, Grafana, ELK stack, OpenTelemetry, and Parquet, alongside extensive experience with Infrastructure as Code (Terraform)
• Deep expertise in database technologies (SQL and NoSQL) and advanced experience with machine learning frameworks (TensorFlow, PyTorch) and MLOps practices for production ML systems