ML Data Engineer

1 week ago

VishakhapatnamVizagBangaloreHyderabad, India Datafoundry Full time

About Datafoundry AI :

At Datafoundry AI, we're a committed team of data scientists, life sciences experts, and technology enthusiasts. Our primary goal is to optimize DL and ML models for maximum impact, offering comprehensive support across all facets of the data-driven business.

Who are we looking for :

We're in search of a dynamic, accountable individual to join our growing organization. In this role, you'll be handling large datasets, creating ML pipelines, and developing DL and ML models.

If you believe you have the requisite skills, personality, and drive to excel in a role that promotes self-improvement and embraces challenges, we encourage you to submit your CV. We're eager to engage in further discussions with potential candidates.

Must have Skills :

1. Python combined with application and ML libraries

2. Elasticsearch / Opensearch

3. REDIS

4. MLFlow or Comet, Kubeflow

5. Azure Databricks

6. NER model development

7. LLM model development

8. Summarization & :

Design, develop, and deploy NLP models for tasks such as Named Entity Recognition (NER), Large Language Model (LLM) development, text summarization, and paraphrasing.

Leverage Python and its rich ecosystem of libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch) to perform data preprocessing, feature engineering, model training, and evaluation.

Integrate NLP solutions with Elasticsearch/OpenSearch for efficient data indexing and retrieval.

Utilize Redis for caching and data persistence to enhance application performance.

Employ MLflow/Comet/Kubeflow for experiment tracking, model management, and collaboration.

Build and execute data pipelines using Azure Databricks to process large-scale text data efficiently.

Conduct research on the latest NLP advancements and stay up-to-date with emerging technologies.

Collaborate effectively with data scientists, engineers, and product managers to ensure successful integration of NLP solutions into production environments.

Write clean, well-documented, and maintainable code.

Continuously monitor and improve the performance of NLP models.

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