Lead Machine Learning Engineer

We are seeking a Lead Machine Learning Engineer - NLP to drive the development of cutting-edge NLP and AI models that enable semantic understanding, real-time inference, and contextual reasoning from structured and unstructured data. This role combines deep expertise in NLP and AI techniques with strong software engineering to deliver scalable, intelligent solutions for the enterprise.

What You’ll Do:

• Lead the design and development of advanced NLP and AI models for enterprise-grade applications.
• Build scalable machine learning pipelines for model training, evaluation, deployment, and monitoring in production environments.
• Apply techniques such as semantic parsing, text classification, summarization, retrieval-augmented generation (RAG), and knowledge graph construction.
• Leverage LLMs and transformer-based architectures (BERT, GPT, T5, etc.) to build domain-specific language models.
• Implement AI methodologies including reinforcement learning, few-shot learning, and zero-shot inference to enhance NLP models.
• Collaborate with product, engineering, and data science teams to integrate intelligent NLP capabilities into applications and platforms.
• Optimize for low-latency inference and ensure high availability of deployed models.
• Design robust evaluation metrics and feedback loops to ensure continuous learning and model performance improvements.
• Stay up to date with the latest research in NLP and AI to incorporate innovative methods into practical applications.

Required Qualifications:

• 7-10 years of experience in applied machine learning, with a strong focus on NLP and AI.
• Expertise with transformer-based models (BERT, RoBERTa, GPT, T5) and deep learning frameworks (PyTorch, TensorFlow).
• Strong programming skills in Python and experience with ML lifecycle tools such as MLflow, Airflow, or Kubeflow.
• Proven experience in deploying and scaling NLP models in production environments.
• Deep understanding of NLP tasks including NER, intent classification, question answering, embeddings, and entity resolution.
• Experience working with large-scale unstructured and semi-structured data sources.
• Familiarity with cloud-native ML development and MLOps best practices.

Preferred Qualifications:

• Experience integrating LLMs using fine-tuning, prompt engineering, or retrieval-augmented generation techniques.
• Knowledge of semantic search, vector similarity, embeddings stores, and knowledge graphs.
• Contributions to open-source NLP/AI libraries or publications in leading AI conferences.
• Familiarity with regulatory and ethical considerations for AI in enterprise environments.