AI ML Engineer

As an AI Engineer at our company, you will be responsible for developing intelligent language-based solutions by leveraging your expertise in Python, Natural Language Processing (NLP), and Large Language Models (LLMs). Your main tasks will involve the following:

- Designing and building NLP pipelines for various tasks such as classification, Named Entity Recognition (NER), summarization, sentiment analysis, and Question & Answering (Q&A). - Fine-tuning and optimizing open-source LLMs like BERT, GPT, and T5 using Hugging Face Transformers. - Preprocessing and structuring large-scale textual datasets using tools such as SpaCy, NLTK, or custom tokenizers.

Additionally, you will work on:

- Integrating LLM APIs (OpenAI, Claude, Cohere, Hugging Face) into real-time applications. - Developing and refining prompt strategies to enhance LLM performance and reliability. - Collaborating with product teams to incorporate LLMs into chatbots, match explainers, recommendation systems, and user-facing tools.

Your responsibilities will also include:

- Deploying models as APIs using Flask, FastAPI, or Streamlit and containerizing them with Docker. - Monitoring model outputs and performance metrics, and making iterations based on evaluations and user feedback. - Keeping abreast of the latest advancements in GenAI, NLP benchmarks, and open-source tools. • *Qualifications Required:** - Proficiency in Python and familiarity with common NLP libraries such as SpaCy, Transformers, NLTK, and Gensim. - Experience in integrating LLM APIs and developing prompt-driven applications. - Hands-on experience with Hugging Face Transformers for training and fine-tuning. - Understanding of key NLP concepts including embeddings, attention, and tokenization. - Knowledge of vector databases like FAISS, Weaviate, or Pinecone (for RAG). - Comfortable with Git, containerization (Docker), and basic REST API development. • *Preferred Bonus Skills:** - Experience with LLMOps tools like LangChain, LlamaIndex, PromptLayer, or similar. - Familiarity with RAG workflows, zero/few-shot techniques. - Knowledge of evaluation metrics such as BLEU, ROUGE, perplexity, and cosine similarity. - Exposure to cloud deployment platforms like AWS, GCP, or Azure. - Contributions to NLP open-source projects or research work would be a plus. As an AI Engineer at our company, you will be responsible for developing intelligent language-based solutions by leveraging your expertise in Python, Natural Language Processing (NLP), and Large Language Models (LLMs). Your main tasks will involve the following:

- Designing and building NLP pipelines for various tasks such as classification, Named Entity Recognition (NER), summarization, sentiment analysis, and Question & Answering (Q&A). - Fine-tuning and optimizing open-source LLMs like BERT, GPT, and T5 using Hugging Face Transformers. - Preprocessing and structuring large-scale textual datasets using tools such as SpaCy, NLTK, or custom tokenizers.

Additionally, you will work on:

- Integrating LLM APIs (OpenAI, Claude, Cohere, Hugging Face) into real-time applications. - Developing and refining prompt strategies to enhance LLM performance and reliability. - Collaborating with product teams to incorporate LLMs into chatbots, match explainers, recommendation systems, and user-facing tools.

Your responsibilities will also include:

- Deploying models as APIs using Flask, FastAPI, or Streamlit and containerizing them with Docker. - Monitoring model outputs and performance metrics, and making iterations based on evaluations and user feedback. - Keeping abreast of the latest advancements in GenAI, NLP benchmarks, and open-source tools. • *Qualifications Required:** - Proficiency in Python and familiarity with common NLP libraries such as SpaCy, Transformers, NLTK, and Gensim. - Experience in integrating LLM APIs and developing prompt-driven applications. - Hands-on experience with Hugging Face Transformers for training and fine-tuning. - Understanding of key NLP concepts including embeddings, attention, and tokenization. - Knowledge of vector databases like FAISS, Weaviate, or Pinecone (for RAG). - Comfortable with Git, containerization (Docker), and basic REST API development. • *Preferred Bonus Skills:** - Experience with LLMOps tools like LangChain, LlamaIndex, PromptLayer, or similar. - Familiarity with RAG workflows, zero/few-shot techniques. - Knowledge of evaluation metrics such as BLEU, ROUGE, perplexity, and cosine similarity. - Exposure to cloud deployment platforms like AWS, GCP, or Azure. - Contributions to NLP open-source projects or research work would be a plus.