Job Title: Applied Scientist
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.- 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.
- 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.
- 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.
Salary
Hourly based
Remote Job
Worldwide
Job Overview
Location
KA , India
