Role Summary :

The Tech R&D Engineer at Netcore Cloud leads advanced research and innovation initiatives, evaluating emerging backend systems, databases, and AI-driven engagement technologies to incubate pioneering capabilities for a high-scale omnichannel marketing platform.

This role focuses on technology scouting, deep benchmarking, experimental design, and technical analysis, with a strong emphasis on building proof-of-concept systems, conducting comparative evaluations of technology stacks, and driving the adoption of innovative engineering practices across Netcore's cloud infrastructure.

Key Responsibilities :

- Conduct in-depth technical research and comparative benchmarking of backend systems and AI pipelines to identify opportunities for improving performance, reliability, and scalability.

- Drive the systematic evaluation of new architectures (e.g., distributed systems, analytical databases, key-value stores, vector databases, and streaming platforms) through rigorous experimentation, scalability studies, and comprehensive technical documentation.

- Lead the incubation and prototyping of emerging technologies, producing detailed analyses, design documents, and recommendations for organizational adoption and integration.

- Continuously scout, evaluate, and analyze frontier technologies relevant to customer engagement, personalization, and real-time decisioning platforms, and present actionable insights to product and platform leadership.

- Collaborate with engineering, data science, and product teams to assess build-versus-buy strategies for new capabilities, providing research findings and experimental evidence to support decision-making.

- Develop proof-of-concept systems and experimental frameworks to validate and stress test innovative approaches in distributed messaging, AI/ML-driven personalization, and ultra-high-throughput computing.

- Author white papers, RFCs, technical reports, and engineering articles to disseminate research findings across Netcore Engineering and the broader technology community.

Minimum Qualifications :

- Bachelor's or Master's degree in Computer Science, Engineering, or a related field, with strong foundations in algorithms, distributed systems, and research methodologies.

- 4 - 8 years of experience in backend engineering, systems research, or a related domain, with demonstrated expertise in evaluating, benchmarking, and analyzing large-scale system architectures and emerging technologies.

Preferred Qualifications :

- Hands-on contributions to open-source projects, research initiatives, or published technical papers related to distributed systems, databases, cloud platforms, or performance benchmarking.

- Experience conducting comparative evaluations of technology stacks (e.g., messaging systems, databases, storage engines, and cloud runtimes), with strong skills in prototype development, experimental design, and data-driven analysis.

- Familiarity with technology incubation and the process of evolving experimental prototypes into production-ready solutions.

Tools and Technologies :

- Extensive experience in GCP & AWS cloud platforms.

- Proficiency in Java+Python (Rust/Golang adds value) for rapid technology prototyping and evaluation.

- Deep understanding of event-driven systems, real-time analytics, Open table formats, and AI/ML integration patterns in cloud-native environments.

- Deep understanding of Parquet/Avro/ORC file formats, Apache Data Sketches & Roaring Bitmaps.

- Deep understanding in Data Structures & Algorithms.

- Deep Understanding of Databases like Cassandra, MongoDB, Aerospike, Clickhouse, BigQuery, Snowflake, Druid, Vector & Graph Databases like Apache Neo4j, TigerGraph.

- Experience in building Data Platforms/Pipelines: Databricks, Apache Iceberg, Apache Hudi, Apache Kafka, Apache Spark, Apache Flink, Apache Trino, Apache Polaris/Project Nessie, Alluxio.

- Extensive experience with system benchmarking tools (YCSB, Sysbench, HammerDB), profiling frameworks, and cloud-native instrumentation stacks.

Impact Metrics :

- Number of successful experimental prototypes incubated and adopted by engineering teams.

- Published benchmarks, whitepapers, or technical reports that inform strategic technology decisions.

- Measurable improvements in platform scalability, reliability, or engagement attributed to research findings and new tech integrations.