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Mid-level Bangalore AI role with common experience band and metro location drives moderate candidate competition.
Strong domain-specific AI, PySpark and trading-surveillance expectations limit cross-industry transferability.
Mandatory AI/ML and PySpark years plus specific RAG/LLM skills make filters stringent.
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Design, develop, and maintain AI data engineering pipelines, focusing on full-stack Python applications integrating frontend frameworks with backend AI services.
Build and optimize Retrieval-Augmented Generation (RAG) systems and implement vector databases to support AI applications relevant to Trading Surveillance.
Develop, maintain, and monitor data pipelines, ETL/ELT workflows, and CI/CD pipelines ensuring performance and reliability for AI/ML workloads.
Minimum 5 years of experience in AI/ML solution implementation (mandatory).
Minimum 3 years of hands-on experience with Pyspark processing high volume data (mandatory).
Strong knowledge of Pyspark internals such as lazy evaluation, window functions, spark architecture, and performance tuning techniques like data skew reduction and join optimizations.
English language proficiency at C2 level (Proficient).
Experience in building Agentic AI systems and pipelines, particularly for code conversion from Q KDB to Pyspark is highly relevant.
Demonstrated ability to design and implement full-stack AI solutions, integrating frontend to backend with AI components.
Strong expertise in advanced AI/ML topics including Multi-Agent System Design, Full-stack AI Integration, Advanced Prompt Engineering, and LLM Observability and Monitoring (LLMOps).