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21 hours ago

Bengaluru, India Movius Full time

Job Title : AI Engineer / Data Scientist NLP & LLM Focus.Location : Bangalore.Role Overview : We are looking for a hands-on AI Engineer / Data Scientist with deep expertise in Natural Language Processing (NLP) and Large Language Models (LLMs). The ideal candidate will have experience building custom AI models from the ground up, optimizing for performance, and integrating them into real-world systems and applications.Key Responsibilities : - Design, build, and deploy NLP and LLM-based models tailored to domain-specific needs (e.g., chatbots, summarization, classification).- Fine-tune and adapt foundation models (e.g., GPT, LLaMA, Mistral) for business use cases.- Build custom pipelines for text processing, entity recognition, language understanding, and speech-to-text integrations.- Work closely with engineering teams to integrate AI models into production systems (APIs, microservices, and edge deployments).- Conduct experimentation, model evaluations, and performance tuning for latency, accuracy, and scalability.- Collaborate cross-functionally with product, infra, and DevOps teams to ensure smooth deployment and monitoring of models.Required Skills & Experience : - Strong experience with Python, PyTorch, or TensorFlow.- Proven track record in training, fine-tuning, and deploying LLMs or transformer-based models (BERT, GPT, T5, etc.).- Solid understanding of NLP fundamentals tokenization, embeddings, attention, text classification, sequence labeling, etc.- Experience with model deployment tools : FastAPI, Flask, Docker, REST APIs.- Exposure to data pipelines and MLOps tools (MLflow, Weights & Biases, SageMaker, etc.).- Knowledge of speech-based interfaces or integration with telephony/speech systems is a strong plus.- Ability to translate business problems into AI solutions.- Experience with RAG (Retrieval Augmented Generation) pipelines.- Familiarity with LangChain, LlamaIndex, or similar agentic/LLM orchestration tools.- Understanding of model security, prompt injection risks, and observability. (ref:hirist.tech)

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