Data Scientist

Key Responsibilities:

- Design develop train validate and deploy machine learning and AI models including LLM-based solutions.
- Build and optimize NLP applications leveraging transformer models and generative AI technologies.
- Perform LLM fine-tuning prompt engineering and model evaluation for domain-specific use cases.
- Develop scalable data processing and machine learning pipelines using PySpark/Spark and Databricks.
- Work with structured and unstructured datasets across SQL and NoSQL environments.
- Collaborate with data engineering and business teams to translate business requirements into AI/ML solutions.
- Conduct exploratory data analysis feature engineering and model performance tuning.
- Implement best practices for model governance validation monitoring and lifecycle management.
- Utilize Azure Cloud services for data storage model training deployment and orchestration.
- Create technical documentation and communicate findings to technical and non-technical stakeholders.

If you would like to learn more please reach out to Elite Technical.

Required Skills:

- 5-8 years of experience in Data Science Machine Learning or AI Engineering roles.
- Strong experience with:
o Large Language Models (LLMs)
o Natural Language Processing (NLP)
o Classical Machine Learning/Data Science algorithms
- Hands-on experience in:
o Python
o PySpark / Apache Spark
o Databricks
o MongoDB or other NoSQL databases
o PostgreSQL and SQL development
o Azure Cloud platform
- Experience designing training validating and deploying machine learning models.
- Knowledge of LLM fine-tuning techniques and model optimization.
- Strong understanding of data structures algorithms and statistical modeling concepts.
- Experience working with large-scale datasets and distributed computing frameworks.
- Strong analytical and problem-solving abilities
- Excellent communication and collaboration skills

Local candidates ONLY
- Seeking candidates with strong expertise in Large Language Models (LLMs) Natural Language Processing (NLP) and classical data science algorithms along with hands-on experience building scalable data and AI pipelines in cloud-based environments.