
NLP/LLM Engineer
1 week ago
We're seeking a skilled NLP/LLM Engineer to join our team and contribute to the design of production-ready NLP pipelines. This is an exciting opportunity for individuals with strong Python skills and experience in working with SpaCy, Transformers (Hugging Face), and NLTK.
The ideal candidate will have hands-on experience fine-tuning/training Transformer models (BERT, T5, GPT variants) and integrating LLM APIs. They should also be comfortable with Docker, Git, and REST API development (Flask/FastAPI). Additionally, experience with vector DBs such as FAISS, Weaviate, or Pinecone would be beneficial.
The role involves designing and implementing NLP pipelines for classification, NER, summarization, sentiment analysis, and Q&A. The successful candidate will work closely with product teams to build and deploy these systems.
- NLP Pipeline Design:
- Design and implement NLP pipelines for classification, NER, summarization, sentiment analysis, and Q&A.
- Data Preprocessing:
- Preprocess and structure large-scale textual datasets using SpaCy, NLTK, or custom tokenizers.
- Model Fine-Tuning:
- Fine-tune and optimize open-source models (BERT, T5, GPT-style) using Hugging Face Transformers.
- LLM Integration:
- Integrate LLM APIs (OpenAI, Anthropic/Claude, Cohere, Hugging Face) into real-time applications and microservices.
- Prompt Engineering:
- Develop prompt strategies and evaluate prompt robustness; implement few/zero-shot approaches where relevant.
- RAG Pipelines:
- Build retrieval + generation (RAG) pipelines with vector DBs (FAISS, Weaviate, Pinecone).
- Model Deployment:
- Deploy model endpoints (Flask/FastAPI/Streamlit), containerize with Docker, and collaborate on cloud deployment.
- Performance Monitoring:
- Monitor model performance, data drift, and user feedback; iterate based on metrics and qualitative signals.
- Collaboration:
- Collaborate with product, infra, and QA to embed models safely and responsibly.
Requirements:
- Strong Python skills and experience with SpaCy, Transformers (Hugging Face), and NLTK.
- Hands-on experience fine-tuning/training Transformer models (BERT, T5, GPT variants).
- Experience integrating LLM APIs and working with prompt engineering.
- Familiarity with embeddings, attention, tokenization, and vector similarity (cosine).
- Experience with vector DBs such as FAISS, Weaviate, or Pinecone.
- Comfortable with Docker, Git, and REST API development (Flask/FastAPI).
- Practical knowledge of evaluation metrics (BLEU, ROUGE, perplexity, cosine similarity).
Bonus Requirements:
- A track record of shipping multiple production LLM/NLP systems end-to-end.
- Strong system design skills (latency/cost tradeoffs, multi-tenant inference).
- Mentoring/leadership experience and ownership of ML lifecycle (MLOps).
Preferred Skills:
- Experience with LangChain, LlamaIndex, PromptLayer, or similar LLMOps tooling.
- RAG workflows and building retrieval pipelines at scale.
- Experience in cloud deployment on AWS/GCP/Azure.
- Contributions to open-source projects or research publications.
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