NLP Engineer

LLM Engineer : (1-3 years)

About the Role

We are looking for an LLM Engineer to design and deploy large language model solutions that power resume parsing, job description understanding, semantic search, and candidate-job matching. Youwill be responsible for developing scalable NLP / LLM workflows, optimizing models for production, and integrating cutting-edge GenAI advancements into TurboHire’s recruitment intelligence platform.

Responsibilities

 Design and implement LLM-powered NLP pipelines for parsing resumes, job descriptions, and other unstructured recruitment text.

 Build scalable systems for information extraction, entity recognition, text classification, and semantic similarity.

 Fine-tune and optimize large language models (e.g., GPT, LLaMA, Falcon, Mistral) for domain-specific applications.

 Develop retrieval-augmented generation (RAG) pipelines with vector databases to enhance semantic search and candidate-job matching.

 Collaborate with software and ML engineers to productionize LLM applications, ensuring high reliability, low latency, and cost efficiency.

 Conduct in-depth text data analysis to uncover insights and improve model performance.

 Continuously evaluate and integrate advances in transformer architectures, embeddings, and LLM toolchains.

 Establish monitoring, evaluation, and safety frameworks to address drift, hallucination, and bias in LLM outputs

• .Requirements

 Bachelor’s or Master’s degree in Computer Science, Data Science, AI / ML, or related field.

 3–5 years of experience in NLP, GenAI, or LLM engineering, with proven exposure to real-world parsing or information extraction.

 Strong programming skills in Python and familiarity with NLP / LLM frameworks (HuggingFace Transformers, LangChain, LlamaIndex, spaCy).

 Hands-on experience with deep learning frameworks (PyTorch, TensorFlow).

 Experience with vector databases (FAISS, Pinecone, Weaviate, Milvus) and embedding-based retrieval.

 Prior experience deploying LLM-powered applications (chatbots, semantic search,resume / job parsing, or similar).

 Understanding of GPU acceleration, distributed inference, and model optimization.

 Familiarity with ranking and retrieval metrics (e.g., precision@k, NDCG) and evaluation ofLLM outputs

 Knowledge of data privacy, fairness, and bias mitigation in AI systems.

 Strong collaboration and communication skills to work with product and engineering teams.