Job Summary:

We are looking for a Senior Software Engineer – AI/ML with deep expertise in developing and deploying cloud-native machine learning solutions. This role demands strong proficiency in Python-based ML libraries, design of scalable microservices, and deep knowledge of NoSQL databases.

The ideal candidate will have hands-on experience with ML algorithms, model performance tuning, model accuracy optimization, and proven delivery of AI applications in areas such as anomaly detection and time series forecasting.

You will work closely with data scientists, cloud engineers, and product teams to build intelligent systems that operate at scale and deliver real-time insights into complex, high-volume data.

Key Responsibilities:

• Design, develop, and deploy AI/ML microservices using Python in a cloud-native environment.
• Build scalable pipelines for training, tuning, and serving ML models in production.
• Integrate and manage NoSQL databases (e.g., MongoDB, ElasticSearch) for efficient storage and retrieval of unstructured or time-series data.
• Optimize model accuracy, latency, and throughput, including hyperparameter tuning, feature engineering, and profiling model performance.
• Lead the development of ML-based solutions for anomaly detection, time series forecasting, and predictive analytics.
• Collaborate with cross-functional teams to translate product requirements into ML-based features.
• Apply best practices in model versioning, A/B testing, and continuous training/validation.
• Ensure high standards of code quality, modularity, and observability in deployed services.
• Evaluate new tools, technologies, and frameworks for ML lifecycle management and monitoring.

Required Skills:

• 5+ years of experience in backend software development using Python.
• Strong programming expertise in Python, with proficiency in ML libraries such as scikit-learn, TensorFlow, PyTorch, XGBoost, or LightGBM.
• Demonstrated experience in implementing anomaly detection algorithms and time series forecasting models (e.g., ARIMA, Prophet, LSTM).
• Good understanding of AI/ML algorithms like Random Forest, K-Means, Autoencoders, Graph Neural Networks (GNNs), and Louvain for anomaly detection, clustering, and time-series analysis.
• Experience in building and deploying cloud-native microservices (e.g., on AWS, Azure, GCP).
• Solid understanding of NoSQL databases like MongoDB, ElasticSearch storing ML data and time series.
• Messaging bus like Kafka or RabbitMQ
• Hands-on experience with model performance tuning, evaluation metrics, and real-world ML system optimization.
• Familiarity with ML lifecycle tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
• Understanding of containerization and orchestration (Docker, Kubernetes) for scalable deployment.
• Proficient in working with Git, CI/CD workflows, and Agile development methodologies.
• Experience with CI/CD tools such as Jenkins, GitLab CI, or GitHub Actions.
• Familiarity with Agile methodologies and ticketing systems (JIRA).

Nice to Have:

• Experience applying ML to wireless network optimization.
• Familiarity with federated learning or edge AI techniques to enable distributed ML across radio/access nodes.
• Understanding of online learning or reinforcement learning for dynamic network adaptation and control.

Education:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.