Machine Learning Engineer

In new product design roles: develops and programs integrated software algorithms to structure, analyze and leverage data in product and systems applications in both structured and unstructured environments. Develops and communicates descriptive, diagnostic, predictive and prescriptive insights/algorithms. In product/systems improvement projects

• Need ML Engineer to develop and enhance telecom product.
• Selected candidate will be working on Telecom Artificial Intelligence Product, on the latest cutting edge technologies!
• We are seeking a highly skilled and experienced Machine Learning Engineer with a strong background in machine learning, deep learning, and extensive experience in implementing advanced algorithms and models to solve complex problems. This role is focused on developing and deploying cutting-edge solutions for anomaly detection, forecasting, event correlation, and fraud detection.

• You will be playing a key role in the next-gen SaaS product and platform development.
• Develop production-ready implementations of proposed solutions across different ML and DL algorithms, including testing on live customer data to improve efficacy, and robustness
• Research and test novel machine learning approaches for analysing large-scale distributed computing applications.
• End-to-End ML Ops Lifecycle: Implement and manage the full ML Ops lifecycle using tools such as Kubeflow, MLflow, AutoML, and Kserve for model deployment.
• Model Implementation: Develop and deploy the machine learning models using PyTorch, TensorFlow ensuring high performance and scalability.
• Distributed Systems: Run and manage PySpark and Kafka on distributed systems with large-scale, non-linear network elements.
• Proficient in Python programming and experienced with machine learning libraries such as Scikit-Learn and NumPy.
• Experience in time series analysis, data mining, text mining, and creating data architectures.
• Processing Approaches: Utilize both batch processing and incremental approaches to manage and analyse large datasets.
• Algorithm Experimentation: Experiment with multiple algorithms, optimizing hyperparameters to identify the best-performing models.
• Cloud Knowledge: Execute machine learning algorithms in cloud environments, leveraging cloud resources effectively.
• Model Retraining: Continuously gather feedback from users, retrain models, and update them to maintain and improve performance.
• Network Domain Expertise: Quickly understand network characteristics, especially in RAN and CORE domains, to provide exploratory data analysis (EDA) on network data.
• Transformer Architecture: Implement and utilize transformer architectures and have a strong understanding of LLM models.
• GAN AI: Apply GAN AI techniques to address network-related use cases and challenges.
• Build, train, and test multiple machine learning models.
• Perform hyperparameter tuning on trained models to achieve the best possible results.
• Make end to end pipelines for machine learning models.
• Act as an individual contributor within the team.
• Interact with a cross-functional team of data scientists, software engineers, and other stakeholders.
• Proven track record of end-to-end machine learning projects.
• Understanding and experience with leading supervised and unsupervised machine learning methods such as regression, neural networks, deep learning, RNN, LSTM, KNN, Naive Bayes, SVM, decision trees, random forest, gradient boosting, ensemble methods, and text mining.
• Experience in MLOps is a plus for the deployment of developed models.
• Work closely with other functional teams to integrate implemented systems into the SaaS platform
• Suggest innovative and creative concepts and ideas that would improve the overall platform
• Create use cases with respect to domain for solving a business problem
• Familiarity with well-known Python packages like Pandas, Numpy, etc., and DL frameworks Keras, TensorFlow, PyTorch. And knowledge of Big Data tools and environment.
• Knowledge of MySQL/No SQL is an added advantage
• Experience on end to end ML models deployment in production
• Proven track record of end-to-end machine learning projects.
• Understanding and experience with leading supervised and unsupervised machine learning methods such as regression, neural networks, deep learning, RNN, LSTM, KNN, Naive Bayes, SVM, decision trees, random forest, gradient boosting, ensemble methods, and text mining.
• Experience in MLOps is a plus for the deployment of developed models.

• Bachelors degree in Science / IT/ Computing or equivalent
• 4 + years of experience in ML Engineering role
• Significant proficiency/in-depth knowledge in the domain (technology and/or products)
• Engineering / Mathematics / Statistics disciplines are acceptable, but candidate must have strong quantitative and applied mathematical skills. Certification courses in Data Science / ML will be an added advantage.
• In-depth working, beyond coursework, familiarity with statistical techniques and current ML techniques, both supervised and unsupervised learning techniques and other ML Techniques.
• Implementation experiences and deep knowledge of Classification, Time Series Analysis, Pattern Recognition, Reinforcement Learning, Deep Learning, Dynamic Programming and Optimization.
• Experience with Telecom Product development with TMF standards preferred
• Experience testing scalable SaaS platform clear advantage.