Machine Learning Engineer - Data Science (3-5 yrs)

About the Role :

We are seeking a highly motivated and experienced Machine Learning Engineer to join our growing team. You will play a key role in developing, optimizing, and deploying machine learning models to solve complex business problems.

This position requires a strong understanding of machine learning principles, hands-on experience with relevant tools and technologies, and the ability to collaborate effectively with cross-functional teams.

Primary Responsibilities :

- Model Development & Optimization : Develop and optimize machine learning models for training, inference, and deployment, selecting appropriate algorithms and techniques based on the problem domain.

- Data Preprocessing & Feature Engineering : Preprocess, clean, and engineer features from both structured and unstructured data sources, ensuring data quality and suitability for model training. This includes handling missing data, outliers, and imbalanced datasets.

- Performance & Scalability : Optimize model performance, considering factors such as accuracy, latency, and resource consumption. Ensure model scalability in production environments, adapting models to handle large volumes of data and user traffic.

- Production Integration : Collaborate with DevOps teams to seamlessly integrate trained ML models into production workflows, automating model deployment and monitoring.

- Model Versioning & Management : Utilize MLflow, DVC, or other model versioning tools to track model experiments, manage model versions, and ensure reproducibility.

Key Skills Required :

- ML Model Development & Deployment : 3+ years of hands-on experience in building and deploying machine learning models in a production setting.

- Programming Skills : Strong programming skills in Python, and familiarity with popular machine learning libraries such as TensorFlow, PyTorch, or Scikit-learn.

- Feature Engineering & Model Evaluation : Proven experience with feature engineering techniques, model evaluation metrics, and model optimization strategies.

- Model Versioning : Familiarity with MLflow, DVC, or similar model versioning and experiment tracking tools.

- Big Data Technologies : Experience working with large-scale datasets and distributed computing frameworks (i.e., Spark, Hadoop).

- Cloud-Based ML Services : Exposure to cloud-based machine learning platforms and services, such as AWS SageMaker, Azure Machine Learning, or Google Cloud AI Platform.

Additional Desired Skills (Optional) :

- Experience with specific machine learning domains (i.e., NLP, computer vision, time series analysis).

- Knowledge of deep learning frameworks and architectures.

- Familiarity with MLOps principles and tools.

- Experience with containerization technologies (Docker, Kubernetes).

- Knowledge of cloud infrastructure and deployment strategies.

- Strong communication and collaboration skills