Vacancy description
IMerit Technology
India, Bengaluru
Data Engineer - ML & AI in Bengaluru, India is listed on Jobeax. Browse 30,000+ vacancies available.
iMerit is a leading AI data solutions company specializing in transforming unstructured data into structured intelligence for advanced machine learning and analytics applications. Our clients span autonomous mobility, medical AI, agriculture, and more—powering next-generation AI systems with high-quality data services.
We are seeking a skilled Data Engineer to help scale and enhance our internal data observability and analytics platform. This platform integrates with data annotation tools and ML pipelines to provide visibility, insights, and automation across large-scale data operations. You will design and optimize robust data pipelines, build integrations with internal platforms (e.g., Design and build scalable batch and real-time data pipelines across structured and unstructured sources.
Integrate analytics and observability services with upstream annotation tools and downstream ML validation systems to enable full-cycle traceability.
Collaborate with product, platform, and analytics teams to define event models, metrics, and data contracts.
Develop ETL/ELT workflows using tools like AWS Glue, PySpark, or Airflow; ensure data quality, lineage, and reconciliation.
annotation throughput, quality KPIs, latency).
Build data models and queries to power dashboards and insights via tools like Athena, QuickSight, or Redash.
Contribute to infrastructure-as-code and CI/CD practices for deployment across cloud environments (preferably AWS).
Document architecture, data flow, and support runbooks; continuously improve platform performance and resilience.
Integrate with customer data platforms and pipelines, including bespoke data frameworks.
4–8 years of experience in data engineering or backend development in data-intensive environments.
Proficient in Python and SQL; Strong experience with cloud-native data tools and services (S3, Lambda, Glue, Kinesis, Firehose, RDS).
Experience with data lake and warehouse patterns (e.g., Delta Lake, Redshift, Snowflake).
Solid understanding of data modeling, schema design, and versioned datasets.
Data Governance and Security: Understanding and implementing data governanc policies and security measures.
Proven experience in building resilient, production-grade pipelines and troubleshooting live systems.
Good working knowledge of Database fundamentals, relational databases and SQL
Experience with observability/monitoring systems (e.g., Familiarity with data governance, RBAC, PII redaction, or compliance in analytics platforms.
Exposure to annotation/ML workflow tools or ML model validation platforms.
Comfort working in Agile, distributed teams using tools like Git, JIRA, and Slack.
You'll work at the intersection of AI, data infrastructure, and impact—contributing to platforms that ensure AI is explainable, auditable, and ethical at scale. Join a team building the next generation of intelligent data operations.
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