Python Developers (AI/ML & Data Engineering)

Junior Role

Senior Role

Lead Role

Responsibilities

• Develop and maintain Python-based applications and data pipelines.
• Assist in building and training ML models under guidance.
• Work with structured and unstructured data sources.
• Perform data cleaning, transformation, and validation.
• Support ETL processes and basic cloud deployments.
• Collaborate with cross-functional teams (Data, AI, Product).

• Design and develop scalable Python applications and data pipelines.
• Build, train, and deploy ML models in production environments.
• Optimize data workflows for performance and reliability.
• Work with big data technologies and distributed systems.
• Collaborate with data scientists and architects on solution design.
• Ensure code quality, security, and best practices.

• Lead end-to-end design and delivery of AI/ML and data engineering solutions.
• Architect scalable, secure, and high-performance data platforms.
• Guide and mentor junior and senior developers.
• Define best practices, coding standards, and architecture guidelines.
• Work closely with stakeholders to translate business requirements into technical solutions.
• Oversee ML model lifecycle, deployment, and monitoring.

Required Skills

• Strong Python programming fundamentals.
• Basic understanding of ML concepts (regression, classification, clustering).
• Experience with Pandas, NumPy, Scikit-learn.
• Knowledge of SQL and databases
• Exposure to ETL tools or data pipelines.
• Familiarity with cloud platforms (AWS/Azure/Google Cloud Platform – basic).

• Advanced Python development experience.
• Strong hands-on experience with ML frameworks (Scikit-learn, TensorFlow, PyTorch).
• Data engineering experience using Spark/PySpark.
• Strong SQL and NoSQL database knowledge.
• Experience with ETL/ELT pipelines.
• Hands-on cloud experience (AWS, Azure, or Google Cloud Platform).
• CI/CD and version control (Git).

• Expert-level Python development.
• Strong architecture experience in AI/ML systems.
• Deep data engineering expertise (Spark, PySpark, Hadoop, Kafka).
• Production ML deployment and MLOps experience.
• Strong cloud architecture experience (AWS/Azure/Google Cloud Platform).
• Experience leading teams and technical decision-making.

Nice to Have

• Exposure to PySpark or Spark.
• Basic knowledge of TensorFlow or PyTorch.
• Understanding of REST APIs and Git.

• Experience with MLOps tools (MLflow, Kubeflow, SageMaker).
• Knowledge of Kafka or real-time data streaming.
• Exposure to containerization (Docker, Kubernetes).

• Experience with GenAI, LLMs, and vector databases.
• Exposure to Snowflake, Databricks, or BigQuery.
• Strong communication and stakeholder management skills.