Machine Learning Engineer (with NLP & AWS Experience)

Machine Learning Engineer

About Caliper:

Caliper is an AI-enabled, comprehensive value-based care (VBC) risk analytics suite designed to provide affordable, accessible and actionable insights. Our solution empowers Community Care Providers to thrive in the VBC landscape, ensuring improved patient outcomes, operational efficiency and financial success.

Position: Machine Learning Engineer (with NLP & AWS Experience)

We are hiring a Machine Learning Engineer who brings strong foundational skills across ML workflows, with a working focus on Natural Language Processing (NLP) and experience deploying ML systems on AWS.

This role is ideal for someone who is technically versatilecomfortable working across diverse machine learning problems including NLP, classification, forecasting, embeddings, and recommender systemswhile being hands-on with modern ML tooling, model lifecycle management, and cloud infrastructure.

Key Responsibilities:

• Build and deploy ML models for a variety of use cases such as classification, prediction, NLP tasks, and recommender systems.
• Design, implement, and maintain end-to-end ML pipelines, including data preprocessing, model training, validation, and deployment.
• Apply NLP techniques where applicable (e.g., sentiment analysis, NER, document parsing, embeddings).
• Leverage AWS services (e.g., SageMaker, Lambda, S3, Bedrock, Comprehend) to deploy and scale ML solutions in production.
• Participate in model evaluation, monitoring, and retraining workflows.
• Collaborate with product, data, and engineering teams to understand requirements and translate them into ML-driven solutions.
• Support both experimental research and production-grade deployment workstreams.

Required Skills & Experience:

• 4+ years of experience as a Machine Learning Engineer or Applied Scientist.
• Strong hands-on experience in core ML techniques: regression, classification, clustering, tree-based models, embeddings, etc.
• Solid Python programming skills and experience with libraries like Scikit-learn, PyTorch or TensorFlow, Pandas, NumPy.
• Exposure to NLP models and libraries (e.g., Hugging Face Transformers, spaCy, NLTK) with practical application experience.
• Experience deploying models using AWS cloud infrastructure, particularly SageMaker, Comprehend, Lambda, or Bedrock.
• Comfortable with model evaluation, metrics (e.g., accuracy, ROC-AUC, F1), and debugging pipelines in production.
• Experience working with version control, CI/CD tools, and basic MLOps practices.

Nice to Have:

• Familiarity with Retrieval-Augmented Generation (RAG) pipelines and vector databases (e.g., FAISS, Milvus, Weaviate).
• Knowledge of prompt engineering or foundation model tuning (e.g., OpenAI, Claude, Bedrock).
• Experience with time series models, anomaly detection, or customer intelligence use cases.
• Exposure to Docker, Kubernetes, or Airflow for workflow orchestration.

What We are Looking For:

• A generalist ML engineer who can adapt to evolving problem statements across NLP, tabular, or other ML use cases.
• Someone who balances code quality and experimentation, and can own model delivery end-to-end.
• A collaborative team player who is curious, self-driven, and excited to build in a fast-paced environment.

Compensation will be commensurate with experience. If you are interested, please send your application to jobs@precognitas.com and

For more information about our work, visit www.caliper.care