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Senior ML Engineer
India
Perfect Fit:
- A Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, Software Engineering, or a related quantitative field.
- 4+ years of experience in designing, building, and deploying end-to-end machine learning pipelines in production environments.
- Proficiency in programming languages such as Python, PySpark, and/or Scala for scalable systems.
- Strong expertise in machine learning frameworks such as scikit-learn, XGBoost, and PyTorch, with hands-on experience in training, tuning, and deploying machine learning models.
- Practical knowledge of data preprocessing and feature engineering, with experience in tools like Pandas, NumPy, and Dask for handling large datasets.
- Proven experience deploying models in production environments, using tools like Docker, Kubernetes, and cloud services (AWS, Azure).
- Expertise in MLOps practices, including CI/CD pipelines, model versioning, and monitoring, using tools like MLFlow, Kubeflow, or TensorFlow Extended (TFX).
- Familiarity with database technologies, including SQL, NoSQL (e.g. MongoDB, Cassandra), and time-series databases (e.g. InfluxDB).
- Knowledge of APIs and integration, including building and consuming RESTful APIs for model serving.
- Strong understanding of cloud platforms (AWS, GCP, Azure) and orchestration tools (e.g. Airflow) for workflow automation.
- Solid foundation in data structures and software engineering best practices, including version control with Git.
Nice-to-Have:
- Experience with feature stores (e.g., Feast, Hopsworks) to manage and reuse machine learning features.
- Hands-on experience with LLMOps tools and deploying large-scale models like LLMs (e.g. GPT, LLaMA) in production.
- Familiarity with graph databases (e.g., Neo4j) or vector databases (e.g., Pinecone, FAISS) for advanced search and retrieval tasks.
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