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6.0 - 11.0 years

12 - 22 Lacs

thiruvananthapuram

Work from Office

Total exp : 6 years Relevant :3 years skills: RAG, LLM, Vector db, GenAI Job Location : Trivandrum Key Responsibilities: Design, build, and optimize RAG pipelines for document-level and multi-turn QA systems. Fine-tune or prompt-tune foundation models (LLMs) for domain-specific tasks. Develop and deploy ML/DL models to support NLP/NLU tasks like summarization, classification, and retrieval scoring. Integrate vector databases, semantic search tools, and embedding models for high-performance document retrieval. Work with unstructured and semi-structured data sources (PDFs, HTML, JSON, SQL, etc.). Collaborate with data engineers, ML engineers, and product teams to build end to-end generative AI solutions. Monitor performance, latency, and relevance metrics; iterate on retrieval and generation models. Implement prompt engineering strategies and hybrid approaches (rule-based + neural) to enhance model reliability. Contribute to research and innovation in applied generative AI, and stay up-to date with the latest in LLM, RAG, and MLOps ecosystems Key Skills Required: Strong experience with RAG architectures and hybrid retrieval systems. • Solid hands-on knowledge of LLMs (e.g., GPT, Mistral, LLaMA, Claude, DeepSeek, etc.) and embedding models (e.g., SBERT, OpenAI, HuggingFace models). Proficiency in machine learning / deep learning using PyTorch, TensorFlow, Hugging Face Transformers, etc. Experience with vector databases (e.g., FAISS, Weaviate, Pinecone, Qdrant). Experience in text chunking, retrieval scoring, prompt tuning, or LoRA/PEFT methods. Strong background in NLP, information retrieval, and knowledge graphs is a plus. Comfortable with Python and associated data science stacks (Pandas, NumPy, Scikit-learn). Experience working with real-world messy data (PDF parsing, OCR, HTML scraping, etc.) If interested kindly call share your resume at clarita.fernandes@sperton.com Regards, Clarita

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