ML OPS Engineer

Summary:

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

Duties & Responsibilities:

Job Description Summary

• Need ML OPS 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, incident classification and KPI capacity prediction,

Hiring Requirements

Job Description

• You will be playing a key role in the next-gen SaaS product and platform development.
• 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.
• Develop and deploy the machine learning models using PyTorch, TensorFlow ensuring high performance and scalability.
• 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.
• 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.
• Good knowledge and architecture understanding on Langchain and Langgraph. Frameworks
• One solid POCs combining multiple LLMs(Mixture of Expert) Llama,,Gpt-4o or any other LLM models
• Build, train, and test multiple machine learning models.
• Perform hyperparameter tuning on trained models to achieve the best possible results.
• Act as an individual contributor within the team.
• Work Closely with 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.
• Proven records of productize the ML models using MLOps
• Extensive experience with Python packages like Pandas, Numpy, Scikit and DL frameworks Keras, TensorFlow, PyTorch.
• Experience with distributed environment and managed with tools such as data processing Spark , Streaming with Kafka highly preferable.
• Must have a good experience on SQL databases.
• At least two end to end ML models deployment in production
• Proven track record of end-to-end machine learning projects.
• Experience with 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.

Additional Job Description

• Bachelors degree in Science/IT/Computing or equivalent
• 4 + years of experience in Data 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.
• Good data analysis and data interpretation skill
• Experience with Telecom Product development with TMF standards preferred
• Experience testing scalable SaaS platform clear advantage.

Pre-Requisites / Skills / Experience Requirements: