Senior Product Software Engineer

Responsibilities

• Build and maintain high‑performance streaming and batch data pipelines that power AI applications, ensuring reliable low‑latency ingestion and high‑throughput processing.
• Implement and extend embedding generation workflows, vector store integrations, and retrieval pipelines supporting semantic search, RAG systems, and AI assistants.
• Develop and optimize scalable storage and retrieval patterns, focusing on cost‑efficient architecture and smooth production performance.
• Implement AI‑optimized data models and storage patterns that align with broader enterprise architecture and platform requirements.
• Integrate pipelines with shared AI platform services (agent frameworks, registries, feature stores), ensuring clean, versioned, and reliable data delivery.
• Build reusable ingestion, transformation, and data processing components that streamline adoption across engineering teams.
• Embed end‑to‑end observability into data systems, including metrics, structured logging, automated alerts, drift detection, and failure analysis.
• Implement robust data quality validation, schema evolution safeguards, and governance/compliance controls.
• Drive execution by owning the full development lifecycle: prototyping, implementation, testing, deployment, optimization, and documentation.
• Collaborate closely with infrastructure, ML engineering, product, and governance teams to deliver production‑ready AI capabilities.
• Lead by example through strong execution, high‑quality code, and proactive problem solving.

• 5+ years of data engineering experience, with at least 1 year in a lead or senior technical role.
• Experience building and scaling streaming data pipelines in large-scale, distributed environments.
• Strong skills in Python, Java and SQL with expert level skill in either Python or Java.
• Proven experience building streaming data pipelines (e.g., Kafka, Flink, Spark, Kinesis).
• Experience with embedding pipelines and vector stores (e.g., Pinecone, Weaviate, FAISS, pgvector).
• Strong knowledge of data modeling, storage optimization, and retrieval patterns for large-scale systems.
• Hands‑on experience with workflow orchestration tools (Airflow, Dagster, etc.).
• Strong collaboration and communication skills, able to partner across AI engineering, infra, and product teams.

• data engineering
• streaming data pipelines
• Python
• Java
• SQL
• data modeling
• storage optimization
• workflow orchestration
• embedding generation
• data quality validation

• collaboration
• communication
• problem solving
• leadership
• execution