Machine Learning Engineer - Artificial Intelligence

Company Description

Terralogic is a leading IT service provider that offers technology services for a connected world. We cover the entire product lifecycle from conceptualization to maturity, decline, and sustenance, emphasizing stability and increased productivity. Our approach focuses on aligning with our customers' roadmaps and business needs to create long-lasting partnerships. With over a thousand skilled professionals and experienced management, we deliver world-class solutions and successes for our customers.

Job experience : 3+ years

Job location : Bangalore

Notice period : Immediate to 15 days

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 :

- 4+ 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, VertexAI).

- 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).