Deploying and operationalizing models in collaboration with engineering teams
Building dashboards and data-driven visual insights for stakeholders
Preparing and preprocessing structured and unstructured data for analysis
Monitoring, optimizing, and retraining models for performance improvements
Partnering with product and engineering teams to align models with business needs
Designing, training, and validating machine learning models
Applied knowledge of statistics and probability for model development
Exposure to large-scale data processing (Spark, Hadoop)
Deep learning frameworks (TensorFlow, PyTorch)
MLOps practices (MLFlow, Kubeflow, CI/CD pipelines for ML workflows)
Experience in data visualization (Seaborn, Plotly, Tableau, or Power BI)
Proficiency in Python with advanced use of NumPy, Pandas, and Scikit-learn
Strong foundation in machine learning techniques (regression, classification, clustering, ensemble methods)
Core (Must-Have) Skills
Hands-on experience with cloud-based ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI)
Expertise in SQL, including complex queries and performance optimization
Experience: 7- 12 years
Business domain knowledge (e.g., retail, finance)
Data pipeline orchestration (Airflow, Prefect)
Tundra’s proven ability to attract, engage and maintain relationships with the most talented resources worldwide is the fuel that empowers this success. Tundra values the talent we work with, and we are invested in creating meaningful relationships, we respect your time and experience and accredit OUR success to YOURS.
Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI
Benefits