Data Scientist / AI-ML Engineer

Job Title: Data Scientist / AI\-ML Engineer<\/span><\/span><\/span>
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Experience:<\/span> <\/span><\/span>2\u20133 Years<\/span> <\/span><\/span><\/span><\/span>
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Locations:<\/span> <\/span><\/span><\/span>Pune /<\/span><\/span> <\/span><\/span>Mumbai (Work<\/span> <\/span><\/span><\/span>From<\/span><\/span> <\/span><\/span>Office)<\/span><\/span><\/span> <\/span><\/span>
<\/span><\/span>Notice Period: Immediate or Max<\/span> <\/span><\/span>15days<\/span><\/span><\/span><\/p><\/div>

Role Overview<\/span><\/span><\/span>
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We are<\/span> <\/span><\/span>seeking<\/span> <\/span><\/span>passionate and innovative<\/span> <\/span><\/span><\/span><\/span>Data Scientists / AI\-ML Engineers<\/span><\/span><\/span> <\/span><\/span>to build next\-generation AI<\/span> <\/span><\/span><\/span>solutions<\/span><\/span> <\/span><\/span>leveraging<\/span> <\/span><\/span>Machine Learning, Deep Learning, Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI technologies.<\/span><\/span><\/span>
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The ideal candidate will have hands\-on experience developing and deploying AI\-powered applications, designing Retrieval\-Augmented Generation (RAG) architectures, working with vector databases, and implementing modern GenAI frameworks. Experience with Knowledge Graph\-based RAG systems will be a significant advantage.<\/span><\/span><\/span>
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Key Responsibilities<\/span><\/span><\/span>
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Design, develop, train, and deploy Machine Learning and Deep Learning models for real\-world business applications.<\/span><\/span><\/span>
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- Perform data preprocessing, feature engineering, model evaluation, and optimization.<\/span><\/span><\/span>
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  - Build NLP solutions using transformer\-based architectures such as GPT, BERT, and related models.<\/span><\/span><\/span>
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    Develop and<\/span> <\/span><\/span>optimize<\/span> <\/span><\/span>Large Language Model (LLM) applications and Generative AI solutions.<\/span><\/span><\/span>
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    Design and implement Retrieval\-Augmented Generation (RAG) pipelines, including document chunking, embedding generation, retrieval mechanisms, and response orchestration.<\/span><\/span><\/span>
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    Work with vector databases to enable semantic search and contextual retrieval.<\/span><\/span><\/span>
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    Develop AI\-powered assistants, chatbots, and intelligent automation solutions.<\/span><\/span><\/span>
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    Collaborate with product, engineering, and business teams to define AI use cases and deliver scalable solutions.<\/span><\/span><\/span>
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    Monitor model performance and continuously improve model accuracy and efficiency.<\/span><\/span><\/span>
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    Stay current with advancements in AI, LLMs, GenAI frameworks, and emerging technologies.<\/span><\/span><\/span>
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    Key Technical Skills<\/span><\/span><\/span>
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    Machine Learning & Deep Learning<\/span><\/span><\/span>
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    Natural Language Processing & LLMs<\/span><\/span><\/span>
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    Generative AI<\/span><\/span><\/span>
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    RAG (Retrieval\-Augmented Generation)<\/span><\/span><\/span>
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    Vector Databases<\/span><\/span><\/span>
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    Critical Differentiator (Highly Preferred)<\/span><\/span><\/span>
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    Knowledge Graph\-Based RAG<\/span><\/span><\/span>
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    Candidates with hands\-on experience building<\/span> <\/span><\/span><\/span><\/span>Knowledge Graph\-powered RAG solutions<\/span><\/span><\/span> <\/span><\/span>and<\/span> <\/span><\/span><\/span><\/span>Hybrid Retrieval Architectures<\/span><\/span><\/span> <\/span><\/span>will be strongly preferred.<\/span><\/span><\/span>
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    Required Qualifications<\/span><\/span><\/span>
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    Bachelor's or<\/span> <\/span><\/span><\/span>Master's degree in Computer Science<\/span><\/span>, Artificial Intelligence, Data Science, Machine Learning, or<\/span> <\/span><\/span><\/span>a related field<\/span><\/span>.<\/span><\/span><\/span>
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    2\u20133 years of hands\-on experience in AI/ML development.<\/span><\/span><\/span>
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    Strong programming skills in Python and AI/ML ecosystems.<\/span><\/span><\/span>
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    Experience building and deploying machine learning and NLP solutions in production environments.<\/span><\/span><\/span>
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    Practical experience with LLMs, Generative AI frameworks, and RAG architectures.<\/span><\/span><\/span>
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    Strong understanding of data structures, algorithms, and software engineering best practices.<\/span><\/span><\/span>
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    Preferred Candidate Profile<\/span><\/span><\/span>
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    Experience developing production\-grade GenAI applications.<\/span><\/span><\/span>
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    Strong understanding of Retrieval\-Augmented Generation (RAG) architectures.<\/span><\/span><\/span>
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    Exposure to Knowledge Graphs and Graph\-Based AI Systems.<\/span><\/span><\/span>
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    Passion for emerging AI technologies, LLMs, and Agentic AI.<\/span><\/span><\/span>
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    Ability to work independently and contribute effectively within cross\-functional teams.<\/span><\/span><\/span>
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