
Hiring For Generative AI Expert For Trivandrum Location
2 days ago
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
Regards,
Clarita
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