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Tier-1 employer, mid-level GenAI role in Bangalore with popular skills creates high applicant competition.
Core ML/GenAI skills transfer across industries, but financial-domain experience adds moderate specificity.
Multiple mandatory technical skills (Python, ML, GenAI, ETL) increase screening rigor despite no explicit years.
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Design, develop, and deploy machine learning models including supervised, unsupervised, and time series techniques for prediction, classification, and optimization.
Build and optimize large language model (LLM) applications such as chatbots, assistants, summarization, and Q&A, including applying Retrieval Augmented Generation (RAG) with vector databases.
Contribute to model evaluation, prompt engineering, LLM integration with APIs and enterprise systems, code reviews, and architecture decisions with exposure to financial domain data and LLM guardrails.
Strong proficiency in Python and SQL programming.
Experience with ML frameworks like Scikit-learn, TensorFlow or PyTorch and GenAI/LLM tools such as OpenAI/Azure OpenAI, LangChain, vector databases.
Experience in data engineering including ETL pipelines, data modeling, and validation.
Bachelor of Engineering degree; Work Experience Required: Not explicitly mentioned in the JD; Location: Bengaluru.
Has practical experience building and deploying ML models in financial domain applications involving risk, portfolio, pricing, or forecasting data.
Demonstrates expertise in GenAI and LLM technologies, including prompt engineering and model fine-tuning using frameworks like Hugging Face.
Possesses skills in cloud platforms (Azure, AWS, or GCP) and Spark/Databricks, indicating ability to handle scalable ML and data engineering workloads.