Lead Engineer- Java FS with Streaming

Responsibilities

• Design, build, and maintain robust, scalable backend services for AI/ML Feature platform using Java, Spring Boot, and modern cloud-native technologies.
• Develop APIs and runtime systems to support low-latency, multi-modal feature serving (aggregation, structured, vector, time-series, graph-based, etc).
• Implement AI/ML computation applications, both real-time and batch, integrating with streaming platforms (eg, Kafka, Dataproc) and storage.
• Ensure data/feature consistency and parity between online and offline environments to avoid training/serving skew.
• Optimize system performance through caching strategies, asynchronous processing, and storage selection tailored to feature types.
• Build and maintain integration layers with upstream data producers and downstream model inference consumers.
• Implement monitoring, alerting, and observability features to ensure production reliability.
• Collaborate cross-functionally with Data Science, MLOps, Infrastructure, and Governance teams to deliver end-to-end capabilities.
• Participate in on-call support rotations and contribute to incident resolution and root cause analysis.

Requirements

• bachelors or masters degree in Computer Science, Engineering, or a related field.
• 5+ years of experience developing backend systems using Java including frameworks like Spring Boot.
• Strong understanding of distributed systems, API design, and multi-threaded programming.
• Practical knowledge of infrastructure components compute, networking, and storage within modern cloud environments.
• Familiarity with microservices architecture, containerization (eg, Docker, Kubernetes), and CI/CD pipelines.
• Passion for performance tuning, low-latency systems, and building for scale.
• Experience working on machine learning platforms or feature stores.
• Hands-on experience with vector databases (eg, FAISS, Milvus), graph databases (eg, Neo4j), or time-series databases (eg, InfluxDB, Prometheus).
• Experience with one or more of the following: BigQuery, HBase, Aerospile, Kafka, Flink, Spark (or Dataproc), or similar systems.
• Solid understanding of the AI/ML development lifecycle, with familiarity in MLOps practices such as model deployment, serving, and monitoring.
• Knowledge about public cloud infrastructure such as Google cloud platform is a strong plus.
• Knowledge of data governance, access control, and privacy practices in feature/data platforms.
• Contributions to open-source ML infrastructure or platform projects is a plus.