Machine Learning Engineer

Experience: 4+ yrs

Timing : 1 – 10 PM IST

Qualifications:

Education:

• 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