Leveraging Expertise in Large Language Models

3 days ago

Tiruppur, Tamil Nadu, India beBeeLanguage Full time ₹ 25,00,000 - ₹ 35,00,000
Job Opportunity

We are seeking a skilled and experienced Large Language Model (LLM) professional to play a critical role in our GenAI-powered products.

The ideal candidate will have 4–6 years of hands-on experience in developing, configuring, and fine-tuning LLMs and Small Language Models (SLMs), Agents.

The successful candidate will work closely with our product and engineering teams to develop domain-specific vertical LLMs, curate datasets, optimize performance, and deploy production-ready models.

Key Responsibilities:

  • Design, configure, and deploy LLM-based solutions including open-source models and APIs (e.g., OpenAI, HuggingFace, Vertex AI).
  • Develop and fine-tune Small Language Models (SLMs) for specific enterprise verticals (e.g., Finance, Legal, Fraud Detection).
  • Write efficient Python code for training pipelines, model orchestration, inference services, and dataset management.
  • Build, clean, and label datasets for supervised fine-tuning and evaluation.
  • Conduct experimentation and benchmarking across models to measure accuracy, latency, and cost-performance trade-offs.
  • Apply Retrieval-Augmented Generation (RAG) techniques, embedding models, and vector database integrations (e.g., Pinecone, FAISS, Weaviate).
  • Ensure security, compliance, and scalability of deployed LLM solutions.
  • Collaborate with cross-functional teams including Product, Backend, and Frontend to integrate models into enterprise platforms.

Required Qualifications:

  • 4–6 years of experience in AI/ML development, with a strong focus on NLP and GenAI.
  • Hands-on expertise in LLMs like GPT, LLaMA, Falcon, Mistral, or similar open models.
  • Proficiency in Python and key libraries (Transformers, LangChain, Pydantic, etc.).
  • Experience with dataset creation, curation, and labeling for fine-tuning tasks.
  • Knowledge of vector databases, embeddings, and similarity search.
  • Familiarity with model serving frameworks (e.g., FastAPI, Flask, Triton).
  • Ability to optimize model inference for latency and cost efficiency.
  • Experience with ML lifecycle tools (MLFlow, Weights & Biases, etc.) is a plus.

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