Senior Software Engineer - Data Engineering & AI

We are seeking a talented and driven Data Engineer + AI Engineer with 2–6 years of experience to join our team. The ideal candidate will be responsible for designing and maintaining scalable data pipelines, managing data infrastructure, and developing AI/ML solutions that support business objectives. This role requires expertise in both data engineering and artificial intelligence, enabling the delivery of end-to-end data-driven and intelligent applications.

Requirements

Core Responsibilities

Design, develop, and maintain scalable data pipelines and ETL/ELT workflows.
Build and optimize data architectures, data warehouses, and data processing systems.
Develop, train, evaluate, and deploy machine learning and AI models.
Collaborate with cross-functional teams to deliver data-driven and AI-powered solutions.
Ensure data quality, integrity, security, and governance across all data systems.
Optimize database performance and large-scale data processing workflows.
Implement and maintain MLOps practices, including model deployment, monitoring, and automation.
Work with cloud-based data and AI services to build scalable solutions.
Troubleshoot and resolve issues related to data pipelines, infrastructure, and AI models.
Stay current with emerging technologies and best practices in data engineering and artificial intelligence.

Qualification

Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Information Technology, Software Engineering, or a related discipline.
2–6 years of professional experience in Data Engineering, AI Engineering, Machine Learning Engineering, or a related field.

Required Skills

Strong proficiency in Python and SQL.
Experience with relational and non-relational databases (PostgreSQL, MySQL, MongoDB, etc.).
Hands-on experience building and maintaining ETL/ELT pipelines.
Knowledge of data orchestration tools such as Apache Airflow or similar platforms.
Experience with big data technologies such as Apache Spark, Kafka, or Hadoop.
Proficiency in machine learning frameworks such as TensorFlow, PyTorch, or Scikit-Learn.
Experience developing and deploying AI/ML models in production environments.
Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).
Understanding of MLOps, model monitoring, CI/CD pipelines, and version control using Git.
Knowledge of Generative AI, LLMs, RAG frameworks, or NLP concepts is a plus.
Strong analytical and problem-solving skills.
Excellent communication and collaboration abilities.
Ability to work independently and manage multiple priorities.
Strong attention to detail and commitment to quality.
Continuous learning mindset with a passion for emerging AI and data technologies