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Tier-1 brand, mid-level ML role in Bangalore with popular GenAI skills drives high competition.
Requires niche LLM, Bedrock/Cortex and finance domain expertise, limiting cross-industry transferability.
Explicit 5-7 years plus required LLM, Bedrock and Cortex expertise produces stringent screening filters.
Develop and deploy GenAI/LLM solutions covering content extraction, semantic search, QA, summarization, reasoning, and recommendation with measurable performance frameworks.
Design and operate prompt-based and RAG-based systems, including orchestration and multi-step reasoning workflows; collaborate with engineering for scalable, production-grade API and pipeline delivery.
Lead data pipeline design for structured/unstructured data, perform applied research translating state-of-the-art NLP methods into improvements, mentor junior scientists, and manage AI governance and responsible AI practices.
Advanced degree (Masters preferred) in Data Science, Computer Science, Machine Learning, Statistics, or related quantitative fields, or equivalent practical experience.
5-7 years of applied experience building ML/NLP solutions with production deployment in fast-paced environments.
Proven expertise in NLP and LLM technologies including prompt engineering, RAG, evaluation methods; hands-on experience with Amazon Bedrock or equivalent managed LLM platforms and Cortex analytics workflows.
Strong Python programming skills; familiarity with ML/DL frameworks (PyTorch or TensorFlow); experience in data pipelines (including embeddings, vector search); software engineering best practices (version control, testing, CI/CD).
Experienced in delivering scalable GenAI solutions in enterprise or financial services environments with deep LLM and prompt engineering expertise.
Skilled in integrating deployed ML/NLP models into production APIs and services, with demonstrated applied research translating academic methods to practice.
Capable of operating across technical leadership, stakeholder communication, and mentoring, including governance of responsible AI and safety evaluation methodologies.