- Design, develop, and implement advanced AI and machine learning models focusing on generative AI and NLP technologies.
- Work with large datasets, applying statistical and machine learning techniques to extract insights and develop predictive models.
- Collaborate with engineering teams to integrate models into production systems.
- Apply best practices for model training, tuning, evaluation, and optimization.
- Develop and maintain pipelines for data ingestion, feature engineering, and model deployment.
- Leverage tools like OpenAI s GPT models, Google Gemini, Microsoft Copilot, and other available platforms for AI-driven solutions.
- Build and experiment with large language models, recommendation systems, computer vision models, and reinforcement learning systems.
- Continuously stay up-to-date with the latest AI/ML technologies and research trends.

Qualifications:

Required:

- Proven experience as a Data Scientist, Machine Learning Engineer, or similar role.
- Strong expertise in building and deploying machine learning models across various use cases.
- In-depth experience with AI frameworks and tools such as OpenAI (e.g., GPT models), Google Gemini, Microsoft Copilot, and others.
- Proficiency in machine learning techniques, including supervised/unsupervised learning, reinforcement learning, and deep learning.
- Expertise in model training, fine-tuning, and hyperparameter optimization.
- Solid programming skills in Python, R, or similar languages.
- Solid understanding of model evaluation metrics and performance tuning.
- Familiarity with cloud platforms (AWS, Azure, Google Cloud) and ML model deployment tools like TensorFlow, PyTorch, and Keras.
- Experience with MLOps tools such as MLflow,



Kubeflow, and DataRobot.
- Strong experience with data wrangling, feature engineering, and preprocessing techniques.
- Excellent problem-solving skills and the ability to communicate complex ideas to non-technical stakeholders.

Preferred:

- PhD or Master s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
- Experience with large-scale data processing frameworks (Hadoop, Spark, Databricks).
- Expertise in Natural Language Processing (NLP) techniques and frameworks like Hugging Face, BERT, T5, etc.
- Familiarity with deploying AI solutions on cloud services, including AWS SageMaker, Azure ML, or Google AI Platform.
- Experience with distributed machine learning techniques, multi-GPU setups, and optimizing large-scale models.
- Knowledge of reinforcement learning (RL) algorithms and practical application experience.
- Familiarity with AI interpretability tools such as SHAP, LIME, and Fairness Indicators.
- Proficiency in using collaboration tools such as Jupyter Notebooks, Git, and Docker for version control and deployment.

Additional Tools Technologies (Preferred Experience):

- Natural Language Processing (NLP): OpenAI GPT, BERT, T5, spaCy, NLTK, Hugging Face
- Machine Learning Frameworks: TensorFlow, PyTorch, Keras, Scikit-Learn
- Big Data Processing: Hadoop, Spark, Databricks, Dask
- Cloud Platforms: AWS SageMaker, Google AI Platform, Microsoft Azure ML, IBM Watson
- Automation Deployment: Docker, Kubernetes, Terraform, Jenkins, CircleCI, GitLab CI/CD
- Visualization Analysis: Tableau, Power BI, Plotly, Matplotlib, Seaborn, NumPi, Pandas
- Database : RDBMS, NoSQL
- Version Control: Git, GitHub, GitLab

📌 Data Scientist (Valsad)
🏢 ENOVADATA
📍 Valsad