Machine Learning Engineer (Java/Scala & Big Data)

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India. The company has 8 years of experience and leadership in enterprise AI, supported by expertise and investment in data, analytics, cloud & DevOps, application modernization, and customer experience.

• Good hands-on coding expertise in Java, Scala, and SQL (including PL/SQL).
• Solid hands-on experience working with Snowflake.
• Proven knowledge of design, architecture, and development using Big Data technologies for large data volumes and transaction systems (Hadoop, Spark, Hive, Kafka).
• Deep coding skills and operational experience with real-time streaming platforms (Apache Flink, Spark Streaming, Apache Pinot).
• Strong decision-making capabilities, effective teamwork, and active listening skills.

Nice to have:

• Proficiency in Python for machine learning and data engineering tasks.
• Proven expertise utilizing NoSQL databases (ClickHouse, MongoDB, Cassandra, HBase, Redis).
• Hands-on experience with scheduling and orchestration tools (Apache Airflow, Control-M).
• Practical experience with containerization and orchestration (Docker, Kubernetes).
• Familiarity with Agile development frameworks and building/maintaining CI/CD pipelines.

• Drive the end-to-end development effort to deliver high-quality data and ML solutions that meet business requirements and architectural vision.
• Design, create, and manage large-scale data pipelines capable of handling complex, high-volume, multi-dimensional data and deploying machine learning models.
• Deliver core capabilities required for creating and optimizing real-time streaming data pipelines.
• Optimize system performance to ensure low-latency querying and high-throughput data ingestion.
• Integrate complex data solutions into existing enterprise systems in close collaboration with cross-functional engineering teams.
• Produce strict project deliverables, including system design, codebase, test cases/results, and comprehensive user documentation.

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