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Position Summary:

As a Principal Machine Learning Engineer, you will be responsible for leading the development and deployment of advanced machine learning models, with a particular emphasis on large language models (LLMs) and natural language processing (NLP) techniques. You will collaborate with data scientists, engineers, and product managers to harness LLMs for solving complex business challenges, ensuring scalability, performance optimization, and production readiness. In this role, you will also set technical standards and mentor junior engineers, establishing best practices in machine learning operations (MLOps), model governance, and responsible AI usage.

• 7+ years of experience developing machine learning models, with a focus on natural language processing (NLP) and large language models (e.g., GPT, BERT, T5).
• 7+ years with Python, with deep knowledge of machine learning libraries like Langchain, Hugging Face Transformers, or NLTK.
• 5+ years experience deploying and fine-tuning LLMs and RAG solutions for various use cases, including text generation, summarization, question-answering, and entity recognition.
• 5+ years experience with large-scale data processing tools and platforms (e.g., Spark , PubSub , Kafka )
• 5+ years hands-on experience with cloud-based ML platforms (Google Vertex AI, Azure OpenAI)
• 5+ years experience deploying end-to-end NLP pipelines, from data preprocessing and feature engineering to model serving and monitoring.
• 7+ years experience with versioning, monitoring, and retraining models in production environments, ensuring continuous model improvements.

• Deep understanding of NLP techniques such as tokenization, embeddings, topic modeling, and transfer learning in the context of LLMs.
• Experience in using pre-trained LLMs and fine-tuning them for domain-specific applications.
• Familiarity with reinforcement learning from human feedback (RLHF) and responsible AI guidelines, especially in LLM deployment.
• Experience working with large-scale text datasets and optimizing data pipelines for efficient training and inference.
• Knowledge of tools and frameworks for MLOps and model lifecycle management (e.g., MLflow, Kubeflow, DVC).
• Familiarity with vector databases and semantic search, particularly in relation to LLM-based applications (e.g., Pinecone, Weaviate).
• Strong leadership experience, with a track record of mentoring technical teams and guiding the strategic application of LLMs.
• Contributions to NLP research or open-source projects involving LLMs, or participation in AI ethics or fairness initiatives.