Responsibilities :

- Design, develop, and implement data-driven solutions using Python in a production environment

- Apply statistical and data mining techniques to extract valuable insights from data

- Develop, train, and deploy machine learning models using libraries like Scikit-learn, NLP libraries, SparkML, PyTorch, etc.

- Utilize Spark for large-scale data processing and manage data pipelines with Delta Lake architecture

- Manage and deploy ML models in production using cloud platforms (AWS, Azure, Google Cloud) or Databricks

- Lead the development and implementation of Machine Learning enabled product platforms

- Ensure efficient and scalable data pipelines and ML workflows with MLOps practices

- Collaborate with data scientists and engineers to bridge the gap between data and software development

- Write clean, well-documented, and performant Python code with strong understanding of data structures and algorithms

- Work effectively with SQL databases (Postgres, MySQL, etc.) and NoSQL databases (MongoDB)

- Explore and utilize different cloud database technologies like Redshift, Delta Lake, Cosmos DB

- Implement Object-Relational Mappers (ORMs) like SQLAlchemy for data persistence

- Integrate DevOps principles and practices into your workflow, including CI/CD pipelines and observability tools

- Build event-driven services and design/deploy ML algorithms for production environments

- Thrive in a fast-paced environment, contributing to the entire product development lifecycle

- Create clear technical documentation and structure release cycles for efficient development processes

- Possess a solid understanding of Linux fundamentals and best practices for writing high-performance code

- Explore Python server frameworks like Flask or FastAPI (bonus points)

- Leverage multithreading and multi-processing techniques (e., Ray) for optimized solutions

Qualifications :

- 6-10 years of experience in server-side Python development for production environments

- 4+ years of experience in Data Science, including statistics, data mining, hypothesis validation, and algorithm design

- 3+ years of experience with Spark programming, Delta Lake architecture, and distributed data processing

- 3+ years of experience managing production deployments in major cloud platforms (AWS, Azure, Google Cloud) or Databricks

- 3+ years of experience as a lead engineer in developing ML-enabled product platforms

- Strong understanding of MLOps concepts and model deployment strategies

- Excellent Python programming skills with a deep understanding of data structures and algorithms

- Proficiency with SQL and RDBMS (e., Postgres, MySQL) and experience with NoSQL databases (e , MongoDB)

- Hands-on experience with cloud database technologies (Redshift, Delta Lake, Cosmos DB, etc.)

- Familiarity with ORM libraries like SQLAlchemy

- Understanding of DevOps practices, CI/CD pipelines, and observability tools

- Experience building scalable data pipelines, ML pipelines, and event-driven services

- Ability to work effectively in a fast-paced environment with zero-to-one product development experience

- Excellent communication and documentation skills

- Strong understanding of Linux fundamentals and best practices for performance optimization