Lead I - Data Science

3 - 7 years

0 Lacs

Posted:2 days ago| Platform: Shine logo

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Work Mode

On-site

Job Type

Full Time

Job Description

As a Machine Learning Engineer at our company, you will play a crucial role in driving business value through intelligent, data-driven solutions. Your primary responsibilities will include: - Analyzing high-volume, complex datasets to identify trends, patterns, and business opportunities. - Designing, developing, and deploying ML models and Large Language Models (LLMs) to address real-world business problems. - Evaluating and selecting between LLMs and traditional ML models based on the specific use case. - Building and optimizing data pipelines for feature engineering and model training. - Deploying models into production using AWS services such as SageMaker, Lambda, EC2, and S3. - Monitoring and maintaining model performance, including retraining and scalability improvements. - Communicating data insights and model results to both technical and non-technical stakeholders. - Collaborating closely with data engineers, analysts, product managers, and domain experts. In order to excel in this role, you should possess the following mandatory skills: - Strong proficiency in Machine Learning, including model development, training, tuning, and evaluation using standard ML algorithms. - Ability to choose between Large Language Models (LLMs) and traditional ML approaches based on specific use cases. - Proficiency in Python and ML libraries such as scikit-learn, Pandas, NumPy, TensorFlow, or PyTorch. - Experience with Cloud Deployment on AWS, specifically with SageMaker, Lambda, EC2, and S3 for scalable model deployment. - Expertise in Data Analysis, including exploratory data analysis (EDA), statistical analysis, and working with large datasets. - Strong command of SQL for querying and manipulating structured data. - Experience in Model Monitoring & Automation, including deploying, monitoring, and automating ML pipelines in production. - Ability to effectively communicate complex ML solutions in a business-friendly language. Additionally, the following skills are considered good to have: - Experience with Large Language Model (LLM) Tools like Hugging Face Transformers or similar frameworks. - Familiarity with Data Pipeline Optimization, including feature engineering best practices and ETL workflows. - Exposure to CI/CD for ML, including MLOps practices and tools such as MLflow, Airflow, or Kubeflow. - Understanding of how ML solutions can impact business metrics in domains like finance, marketing, or operations. - Proficiency in using visualization tools like Matplotlib, Seaborn, or Plotly. With your expertise in Data Analysis, Machine Learning, AWS, and SQL, you are well-equipped to contribute effectively to our team and help drive innovative solutions using data-driven approaches.,

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