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Mid-level metro ML/LLM role with broad requirements increases candidate competition.
ML/NLP skills transfer across industries, though domain-specific fraud experience increases fit.
Explicit 4–8 years and mandatory LLM/NLP/ML plus deployment skills enforce strict filters.
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Design, build, and deploy end-to-end AI/ML solutions focused on LLMs, NLP applications, and anomaly detection for fraud and operational monitoring.
Develop and fine-tune NLP models using Hugging Face Transformers and LLM frameworks such as OpenAI, Azure OpenAI, LangChain, and LlamaIndex.
Build scalable ML pipelines and deploy models via API frameworks while collaborating with cross-functional teams to deliver AI solutions and communicate insights.
4-8 years of experience in Data Science or Machine Learning.
Strong proficiency in Python with libraries such as Pandas, NumPy, Scikit-learn, and SciPy.
Hands-on experience with Hugging Face Transformers, LangChain, LlamaIndex, OpenAI APIs, and deep learning frameworks (PyTorch or TensorFlow).
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
Experienced in deploying NLP solutions including named entity recognition, text classification, and semantic search using state-of-the-art transformer models.
Skilled in designing and implementing anomaly detection techniques including Statistical, ML-based, Deep Learning, and Time-Series approaches in production environments.
Comfortable working with cloud platforms (AWS, Azure, or GCP), containerization (Docker), REST API development, and ML pipeline automation technologies.