Lead Data Scientist

- Develop and maintain scalable applications using Python, following best practices for clean, efficient, and maintainable code.

- Build APIs and backend services using modern frameworks such as Flask, FastAPI, or Django.

- Work with containerization and orchestration technologies to support deployment and scalability of applications.

- Design, develop, and deploy GenAI and LLM-based solutions using frameworks such as LangChain and LangGraph.

- Apply prompt engineering techniques and support LLM fine-tuning to optimize model performance.

- Build and optimize data pipelines and perform data processing and transformation using tools like Pandas, NumPy, SQL, and ideally PySpark.

- Translate business requirements into scalable, production-ready technical solutions.

- Collaborate with cross-functional and offshore teams, including UK-based stakeholders, ensuring clear communication and alignment.

- Manage multiple projects in a fast-paced environment while maintaining delivery quality and timelines.

Requirements

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related discipline.

- Minimum 3+ years of experience in software engineering, data science, machine learning, or AI engineering roles.

- Strong proficiency in Python with hands-on experience in backend development and API frameworks.

- Experience in deploying GenAI/LLM applications in production environments.

- Solid understanding of containerization, CI/CD pipelines, and modern deployment practices.

- Experience working with cloud platforms, preferably Azure, along with Databricks exposure.

- Strong skills in data manipulation and analysis using Pandas, NumPy, SQL, and PySpark (preferred).

- Familiarity with distributed data processing frameworks such as Spark is an added advantage.

- Excellent communication and collaboration skills, especially in distributed/offshore team environments.

Originally posted on Himalayas