Al/ml- Intern

2 weeks ago

Bengaluru Karnataka, India Epifi Technologies Full time

**About Us**

**Role Overview**

We’re looking for a passionate and curious Machine Learning Intern to join our team. You’ll work closely with fellow data scientists and engineers to develop, experiment and deploy ML models that solve real-world problems and improve user experiences.

**Responsibilities**:
- Batch Deployment - Build scalable data, features and training pipelines and help productionize ML models.
- Experimentation - Build A/B experimentation framework and run experiments at scale by leveraging features and training pipelines.
- Realtime Deployment - Develop a real-time service to deploy ML models that serve thousands of requests every day, and set up monitoring/evaluation to assess the health of the service and model performance [technical metrics].
- Pattern Identification - Identify critical patterns in the large datasets using diverse tools [Python libraries, Spark, BigQuery etc.] to solve business problems.**Requirements**:
- Energy - Eagerness to learn and contribute in a collaborative team environment.
- Coding - Currently pursuing or recently completed a bachelor's degree with strong programming skills in Python.
- Machine Learning - Good understanding of ML concepts and familiarity with scikit-learn, TensorFlow, or PyTorch.
- Familiarity with data wrangling, feature engineering, and model evaluation.
- Good problem-solving and communication skills.

**Nice to have**:
- Exposure to MLOps tools or workflows (e.g. Docker, Airflow, PySpark).
- Knowledge of cloud platforms (GCP, AWS, or Azure).
- Experience with LLMs, NLP, or deep learning models.

**What you will gain**:
- Hands-on experience with real-world ML projects
- You will build scalable data and complex feature pipelines that will be instrumental in the deployment of ML models, run experiments at scale on millions of datapoints.
- Experience in developing, deploying and maintaining real-time services.
- Mentorship from experienced engineers and data scientists.
- A letter of recommendation or full-time conversion potential based on performance.

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