ML Engineer with NLP + AWS

We are looking for a skilled Machine Learning Engineer with strong expertise in Natural Language Processing (NLP) and AWS cloud services to design, develop, and deploy scalable ML models and pipelines. You will play a key role in building innovative NLP solutions for classification, forecasting, and recommendation systems, leveraging cutting-edge technologies to drive data-driven decision-making in the US healthcare domain.

Key Responsibilities:

• Design and deploy scalable machine learning models focused on NLP tasks, classification, forecasting, and recommender systems.
• Build robust, end-to-end ML pipelines encompassing data ingestion, feature engineering, model training, validation, and production deployment.
• Apply advanced NLP techniques including sentiment analysis, named entity recognition (NER), embeddings, and document parsing to extract actionable insights from healthcare data.
• Utilize AWS services such as SageMaker, Lambda, Comprehend, and Bedrock for model training, deployment, monitoring, and optimization.
• Collaborate effectively with cross-functional teams including data scientists, software engineers, and product managers to integrate ML solutions into existing products and workflows.
• Implement MLOps best practices for model versioning, automated evaluation, CI/CD pipelines, and continuous improvement of deployed models.
• Leverage Python and ML/NLP libraries including scikit-learn, PyTorch, Hugging Face Transformers, and spaCy for daily development tasks.
• Research and explore advanced NLP/ML techniques such as Retrieval-Augmented Generation (RAG) pipelines, foundation model fine-tuning, and vector search methods for next-generation solutions.
  

Required Qualifications:

• Bachelors or Masters degree in Computer Science, Engineering, or a related technical field.
• 6+ years of professional experience in machine learning, with a strong focus on NLP and AWS cloud services.
• Hands-on experience in designing and deploying production-grade ML models and pipelines.
• Strong programming skills in Python and familiarity with ML/NLP frameworks like PyTorch, Hugging Face, spaCy, scikit-learn.
• Proven experience with AWS ML ecosystem: SageMaker, Lambda, Comprehend, Bedrock, and related services.
• Solid understanding of MLOps principles including version control, model monitoring, and automated deployment.
• Experience working in the US healthcare domain is a plus.
• Excellent problem-solving skills and ability to work collaboratively in an agile environment.
  

Preferred Skills:

• Familiarity with advanced NLP techniques such as RAG pipelines and foundation model tuning.
• Knowledge of vector databases and semantic search technologies.
• Experience with containerization (Docker, Kubernetes) and cloud infrastructure automation.
• Strong communication skills with the ability to translate complex technical concepts to non-technical stakeholders.