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 (key 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

📌 Machine Learning Engineer (Bengaluru)
🏢 GTS TechLabs
📍 Bengaluru