Machine Learning Engineer
- Posted 2 months ago
- Be among the first 10 applicants
Job Description
Skills/Qualifications:
- Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field.
- 5-6 years of hands-on experience in developing and deploying machine learning models.
- Proficiency in programming languages such as Python, R, or Java.
- Strong understanding of forecasting & revenue ERP environments (e.g.: Salesforce & SAP ECC)
- Strong knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn).
- Experience with data preprocessing, feature engineering, and model evaluation techniques.
- Familiarity with cloud platforms (e.g., Azure & AWS) and containerization tools (e.g., Docker, Kubernetes)
- Excellent problem-solving and critical-thinking skills.
- Effective communication and collaboration skills.
More Info
Key Skills
About Company
Skills:
Tensorflow, Pytorch, Python, MLops, Spark, Databricks, GPU infrastructure, model versioning, event tokenization, efficient attention, attention-based architectures, positional embeddings, mixed-precision training, self-supervised pretraining, gradient accumulation, automated retraining, experiment tracking, masked modeling, deep learning models, distributed strategies, contrastive learning, check-pointing, downstream fine-tuning, Production Monitoring
Skills:
Tensorflow, Nlp, Jax, Pytorch, Python, ASR, TTS, Llm
Skills:
Java, Docker, Kubernetes, Python, TFX, AWS SageMaker, Kubeflow, Google Vertex AI, Go, MLflow, Metaflow
Skills:
data engineering , Numpy, Pandas, Pytorch, Python, Testing and release, scikit-learn, Development processes, ELT data pipelines, ML research methodologies, Build processes, Production ML systems, Source control management, ML pipelines
Skills:
error analysis , Deduplication, Pytorch, Python, Cloud Storage, Statistical Analysis, data augmentation, model training, model-based labeling, distributed compute, Active Learning, VLMs, Hugging Face LLMs, ablation studies, dataset curation, benchmark evaluation, workflow orchestration, weak supervision, Batch Processing, inference evaluation, synthetic-data generation, Production Monitoring, data-quality measurement, diffusion models, multimodal models