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