The AI/ML Engineer will be focused on operationalizing our machine learning initiatives.

This role requires an engineering mindset to build robust, production-ready pipelines that move models from experimentation to large-scale deployment, working closely with Data Scientists and DevOps teams.

Key Responsibilities :

- Design, build, and maintain scalable ML pipelines (Training and Inference) using technologies like Kubeflow, Airflow, or MLflow.

- Containerize and deploy trained ML models as low-latency services using Docker and orchestrate them on Kubernetes clusters.

- Optimize model serving infrastructure for high throughput and low latency, including implementing techniques like batch processing and distributed training.

- Collaborate with Data Scientists to refactor experimental code into production-grade, efficient, and well-tested code in Python.

- Implement monitoring and logging strategies for production models, tracking drift, performance, and resource usage.

- Manage cloud-based data and compute resources specifically tailored for ML workloads (AWS Sagemaker, GCP AI Platform, or Azure ML).

Technical Skills Required :

- 4+ years of experience in Software Engineering, MLOps, or ML Infrastructure.

- Expert proficiency in Python and writing clean, maintainable, and efficient production code.

- Mandatory experience with Docker and deploying services on Kubernetes.

- Hands-on experience with at least one ML pipeline orchestration tool (Airflow, Kubeflow, or MLflow).

- Strong understanding of CI/CD principles applied to ML code and model artifacts.

- Experience with major cloud platforms (AWS/GCP/Azure) and their specific ML services.

- Familiarity with ML frameworks like TensorFlow or PyTorch