Senior Software Engineer - Data License Analytics, Pune

Build GenAI tools and AI agents — RAG pipelines, contextual embedding systems, and MCP tool servers that give LLM agents structured access to live pipeline data

Build and maintain data ingestion pipelines into Apache Iceberg, orchestrated via Apache Airflow

Develop semantic search capabilities using vector databases and hosted embedding models

Build self-service ingestion APIs enabling partner teams to onboard to the platform without managing Iceberg or storage infrastructure

Design microservices and backend APIs — with opportunities to contribute to React/TypeScript interfaces embedded in production tools

Instrument systems with OpenTelemetry and contribute to anomaly detection capabilities on the near-term roadmap

6+ Software engineering experience in production environments

Proficiency in Python or a similar language

Familiarity with relational databases and SQL

Interest or experience in data engineering — pipelines, batch processing, or data lake technologies

Understanding of distributed systems and service architecture

Interest in AI/ML — particularly LLMs, RAG, or embedding-based search

Experience with Apache Iceberg, PyArrow, or data lake engineering

Familiarity with Apache Airflow or workflow orchestration

Experience with vector databases, embedding models, or semantic search

Background in anomaly detection or data quality frameworks

Exposure to MCP, A2A, or agent communication protocols

Experience with observability tooling — tracing, metrics, structured logging

Curiosity about financial data and reliability engineering

Ship GenAI tooling used daily in production — MCP servers, RAG pipelines, and AI agents for real incident triage

Be a founding member of a greenfield engineering hub — shape the culture and standards from day one

Collaborate with engineers across Dublin, London, and New York

Work at scale — millions of securities, billions of data points, clients depending on it around the clock

Join a team that values curiosity, learning, and measurable impact