Bachelor s or Master s degree in Computer Science, Applied Mathematics, Engineering, or a related quantitative discipline; PhD s preferred
Specialization or research in applied machine learning, MLOps, or ML systems preferred

Experience:

4+ years of experience designing, developing, and deploying ML models in production environments
1+ year of experience in areas such as recommendation systems, pattern recognition, NLP, or time series modeling
Experience with production-grade Python (preferred), as well as Java or C/C++
Hands-on experience with large-scale software architecture, APIs, and model versioning systems

Technical Skills:

Expertise in Python and ML frameworks such as PyTorch, TensorFlow, or scikit-learn
Proficient in cloud-based ML platforms (e.g., Azure ML, Google Cloud Platform, AWS SageMaker)
Solid understanding of machine learning algorithms (e.g., classification, regression, SVMs, ARIMA, ensemble methods, deep learning, neural network)
Strong foundation in probability theory and statistical modeling (generative and discriminative)
Familiarity with DevOps/MLOps practices, CI/CD pipelines, GitHub Actions, Terraform, Docker, and Kubernetes
Ability to communicate technical concepts clearly to both technical and non-technical stakeholders
Strong collaboration skills with cross-functional teams (engineering, analytics, product)
Ability to independently manage tasks and thrive in a remote-first or hybrid environment

Preferred Skills

:

Experience in regulated industries (e.g., finance, healthcare, insurance)
Excellent communication and stakeholder engagement skills
Strong understanding of deep learning architectures (e.g. CNNs, RNNs, Transformers, GANs)
Strong in GPU based accelerating computing technologies (CUDA, Rapids, NeMo, NIM, etc.)
Proficiency in model evaluation, distributed training, and hyperparameter optimization
Proficient in Big Data Theory based large scale data streaming and in-memory database technologies (Spark, Kafka, Redis, Elastic Search)
Strong in automated workflow technologies (GitHub Actions, Terraform, Helmet) and containerization technologies (Docker, Kubernetes)
Proficient in API and Microservices technologies
Track records in large-scale, real-time AI/GenAI/ML database and solution technologies
Background in responsible AI/ML, model interpretability, and fairness auditing

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