Job Summary:

We are looking for a Software Engineer – AI/ML with good experience in developing and deploying cloud-native machine learning solutions. This role demands proficiency in Python-based ML libraries, design of scalable microservices, and 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:

• 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.
• Contribute towards 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.
• Ensure high standards of code quality, modularity, and observability in deployed services.

Required Skills:

• 3+ 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.
• 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).
• Good understanding of NoSQL databases like MongoDB, ElasticSearch storing ML data and time series.
• Messaging bus like Kafka or RabbitMQ
• Familiarity with ML lifecycle tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
• Understanding of containerization and orchestration (Docker, Kubernetes) for scalable deployment.
• Experience 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).

Education:

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

Job Summary:

We are looking for a Software Engineer – AI/ML with good experience in developing and deploying cloud-native machine learning solutions. This role demands proficiency in Python-based ML libraries, design of scalable microservices, and 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:

• 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.
• Contribute towards 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.
• Ensure high standards of code quality, modularity, and observability in deployed services.

Required Skills:

• 3+ 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.
• 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).
• Good understanding of NoSQL databases like MongoDB, ElasticSearch storing ML data and time series.
• Messaging bus like Kafka or RabbitMQ
• Familiarity with ML lifecycle tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
• Understanding of containerization and orchestration (Docker, Kubernetes) for scalable deployment.
• Experience 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).

Education:

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