Job Description

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.