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