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Senior, niche NLP leadership reduces applicants, but Bangalore ML leadership roles remain moderately competitive.
Requires deep ML/NLP, LLM and domain consulting expertise, so skills are not easily transferable across industries.
Explicit 16–20 years requirement plus mandatory deep-learning, NLP and consulting experience raises screening strictness.
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Lead design and deployment of advanced NLP and ML solutions including entity recognition, intent identification, classification, and summarization using traditional models and large language models (LLMs).
Own end-to-end solution development for NLP applications, including model fine-tuning, evaluation, and integration into enterprise systems across multiple text/audio/image/video modalities.
Collaborate with cross-functional teams and clients in financial services and banking to align solutions with business needs, manage multiple projects, and contribute to firm growth through training and client relationship management.
16-20 years experience with deep learning frameworks such as Keras, PyTorch, TensorFlow and NLP tools like Gensim, SpaCy, Stanford NLP, HuggingFace.
Consulting experience in Financial Services or Banking domain for at least 3 years.
Proficient in Python, SQL, and application development frameworks such as Docker, FastAPI/Django/Flask; experience with cloud platforms Azure, AWS or GCP (certification preferred).
Bachelor's or master's degree in Computer Science or related technical field, or equivalent; strong project/program management skills with ability to handle multiple work streams.
Senior professional with deep technical expertise in NLP and generative AI technologies including prompt engineering, model quantization, fine-tuning, and GPU utilization.
Experienced in consulting and client engagement within financial services/banking, able to quantify business impact and contribute to commercial growth.
Operates with strong program and project management capabilities, balancing technical delivery with business acumen and client relationship management across multiple concurrent projects.