MLOPS Applications Scientist

Job Description: As a MLOPS Engineer at SAVYMINDS, you will be responsible for developing and implementing advanced analytics models to extract actionable insights from complex datasets. Design, implement, and optimize machine learning models that solve business challenges. Create and manage centralized feature stores that serve as a repository for features used in machine learning models. Ensure that features are well-documented, versioned, and easily accessible for model training and deployment. Deployment, monitoring, and continuous improvement of machine learning models in production environments. You will work closely with Data Engineers and BI to ensure models are efficiently deployed, tracked, and maintained throughout their lifecycle.

Key Responsibilities:

• Develop and validate predictive models using supervised, unsupervised, and semi-supervised learning techniques.
• Perform data exploration, preprocessing, and feature engineering.
• Continuously monitor and improve model performance, ensuring accuracy and relevance.
• Provide detailed documentation and communicate findings to stakeholders.
• Develop scalable ML models, from prototyping to production deployment.
• Implement one-click deployments and continuous monitoring for ML models.
• Document model architectures, hyperparameters, and performance metrics.
• Design and implement feature stores for centralized feature management.
• Ensure seamless data integration and versioning for consistent model training.
• Document feature engineering processes and maintain feature metadata.
• Optimize feature store for scalability and performance.
• Automate model deployment pipelines and manage infrastructure for ML models.
• Monitor model performance and implement strategies for continuous improvement.
• Ensure compliance with governance and security standards.

Qualifications:

• Proficiency in Python, Data analysis, feature engineering, modeling, parameter tuning, and mlops libraries.
• Strong understanding of machine learning algorithms and statistical methods.

• Experience with cloud platforms and model deployment.

• Familiarity with cloud platforms and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Proficiency in data engineering tools and frameworks (e.g., Apache Spark, Kafka).
• Knowledge of data versioning, lineage tracking, and data governance.
• Experience with ML Ops tools (e.g., Kubeflow, MLflow).
• Strong programming and automation skills (e.g., Python, Docker, Kubernetes).

Soft Skills:

• Strong problem-solving and analytical thinking.
• Ability to communicate complex technical concepts to non-technical stakeholders.
• Team-oriented with excellent collaboration skills.