Machine Learning Engineer

We are seeking a skilled Machine Learning Engineer to design, develop, and deploy AI-driven models for fraud detection and traffic filtering within our SMS and Voice Firewall solutions. This role focuses on building scalable, real-time analytics systems leveraging machine learning, anomaly detection, and large-scale data processing to enhance telecom security and intelligence.
Key Responsibilities
● Design, develop, and deploy machine learning models to detect spam, fraud, and grey routes in SMS and voice traffic.
● Implement real-time anomaly detection and predictive analytics for telecom datasets.
● Build and optimize scalable data processing pipelines using big data frameworks such as Kafka, Spark, Flink, and Hadoop.
● Perform data preprocessing, cleansing, and normalization for both structured and unstructured datasets.
● Develop and deploy ML models using frameworks such as TensorFlow, PyTorch, and Scikit-learn.
● Ensure model performance, interpretability, and compliance with data security and regulatory standards.
●Collaborate with data engineering and domain teams to continuously improve model accuracy and system performance.
Required Skills & Competencies
●Strong understanding of machine learning techniques including supervised and unsupervised learning, anomaly detection, and NLP.
● Proficiency in Python (TensorFlow, PyTorch, Scikit-learn) and SQL.
● Experience with data processing and analysis using Pandas, NumPy, and Spark.
● Hands-on experience with big data and streaming technologies such as Kafka, Spark, Flink, and Hadoop.
● Familiarity with relational and NoSQL databases including MySQL, PostgreSQL, MongoDB, and Cassandra.
● Experience in deploying ML models using containerization and cloud platforms (Docker, Kubernetes, AWS SageMaker, Azure ML, Google AI Platform).
Preferred Qualifications
●Experience with MLOps practices and tools such as MLflow, Kubeflow, and Airflow for CI/CD of ML models.
● Exposure to cloud-based AI/ML ecosystems (AWS, GCP, Azure).
● Relevant certifications in AI/ML, Data Science, or Telecom Security.
🔹 Core Machine Learning
Machine Learning
Supervised Learning
Unsupervised Learning
Classification & Regression
Model Evaluation & Validation
Cross-Validation
Hyperparameter Tuning
🔹 Advanced ML / Deep Learning
Deep Learning
Neural Networks
Representation Learning
Embeddings & Similarity (Cosine Similarity)
🔹 NLP (very relevant for our work)
Natural Language Processing (NLP)
Text Classification
Text Preprocessing
Tokenization & Vectorization
Semantic Analysis
🔹 Applied ML
Fraud Detection Models
Predictive Modeling
Anomaly Detection
🔹 Feature & Data Work
Feature Engineering
Feature Selection
Data Preprocessing
Handling Imbalanced Datasets
🔹 MLOps (important for credibility)
MLOps
Model Deployment
Model Monitoring
MLflow
🔹 AI / LLM
Large Language Models (LLMs)
Prompt Engineering
Generative AI
Semantic Search
Best 15–20 skills to actually pick (recommended)
If you want a strong, focused profile, use these:
Machine Learning
Deep Learning
Natural Language Processing (NLP)
Text Classification
Feature Engineering
Model Evaluation
Predictive Modeling
Fraud Detection
Anomaly Detection
MLOps
Model Deployment
MLflow
Large Language Models (LLMs)
Prompt Engineering
Semantic Search

Work arrangement
No