Back
Tier-1 brand and Bangalore location increase applicant density despite specialized GenAI skills.
Strong finance-data and GenAI infrastructure requirements reduce cross-industry transferability.
Multiple mandatory specialist skills and a 7+ years requirement will enforce strict shortlisting.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and operate scalable, low-latency data pipelines, feature stores, and vector databases to support ML models and LLM applications at financial market scale.
Develop and deploy end-to-end machine learning pipelines including preprocessing, training, evaluation, and production serving with CI/CD automation.
Collaborate with research, product, and platform teams to translate AI innovations into production-grade, observable systems with on-call ownership.
7+ years of hands-on data engineering or ML engineering experience in production environments.
Strong expertise with LLM/Generative AI technologies and frameworks such as LangChain or LlamaIndex, including experience with RAG pipelines and prompt engineering.
Proficient programming skills in Python (essential) and familiarity with Node.js/TypeScript (preferred).
Deep experience with SQL databases (at least two of ClickHouse, PostgreSQL, Snowflake), NoSQL systems (Elasticsearch, Redis), Apache Kafka streaming, AWS cloud services, and CI/CD tools (GitHub Actions or Jenkins).
Senior-level engineer capable of independently designing resilient, observable large-scale AI data and ML infrastructure supporting financial data and real-time requirements.
Hands-on experience integrating and fine-tuning LLMs and managing production ML lifecycle end-to-end with strong system design acumen including failure mode handling and automation.
Comfortable working closely with research, product, and engineering teams to operationalize advanced AI research into robust, production systems with mentoring experience.