Applied Scientist

Job Title: Applied Scientist
Location: 100% Remote (India)
Duration: 12-Month Contract

About the Role

We are seeking a highly skilled Applied Scientist with expertise in machine learning, large-scale ML systems, and MLOps practices. In this role, you will design, develop, and deploy production-grade ML solutions while collaborating closely with cross-functional teams in engineering, analytics, and product.

Key Responsibilities

• Design, develop, and deploy ML models into production environments with a focus on scalability and reliability.

• Build and optimize recommendation systems, NLP models, time series forecasts, or pattern recognition solutions.

• Contribute to large-scale software architectures, APIs, and model versioning systems.

• Implement ML workflows with CI/CD, containerization, and orchestration tools.

• Work on distributed model training, hyperparameter optimization, and GPU-accelerated deep learning.

• Collaborate with teams to ensure responsible AI/ML practices, interpretability, and fairness in deployed models.

• Engage with stakeholders to communicate technical concepts in a clear and actionable manner.

Required Qualifications

• PhD in Computer Science, Applied Mathematics, Engineering, or a related quantitative field.

• 4+ years of experience designing and deploying ML models in production.

• 1+ year of experience in recommendation systems, NLP, time series, or pattern recognition.

• Strong proficiency in Python (preferred), with additional experience in Java or C/C++.

• Hands-on experience with ML frameworks: PyTorch, TensorFlow, or scikit-learn.

• Proficiency with cloud-based ML platforms: AWS SageMaker, Azure ML, or GCP AI.

• Strong foundation in probability theory, statistics, and machine learning algorithms.

• Familiarity with MLOps/DevOps: CI/CD pipelines, GitHub Actions, Docker, Kubernetes, Terraform.

• Strong collaboration and communication skills, with ability to work independently in a remote-first environment.

Preferred Qualifications

• Experience in regulated industries (finance, healthcare, insurance).

• Strong understanding of deep learning architectures: CNNs, RNNs, Transformers, GANs.

• Expertise in GPU-based accelerated computing (CUDA, RAPIDS, NeMo, NIM, etc.).

• Proficiency in Big Data technologies: Spark, Kafka, Redis, Elastic Search.

• Strong background in API and microservices architecture.

• Experience with real-time AI/ML solutions, distributed training, and automated workflows.

• Background in responsible AI, interpretability, and fairness auditing.