Required Data Engineer – ML & GenAI Platform
Job Description
As a data engineer in our data science group, youll build, use, and enhance our ML and GenAI platforms, powering data products and LLM-based services across. Youll design and operate data and MLOps infrastructure for batch inference, knowledge-base workflows, evaluation metrics, and fine-tuning pipelines. Collaborating with data scientists, product managers, and engineers, youll bring AI features into production at scale.
Extend and implement new capabilities in ML and GenAI platforms
Design and maintain scalable, reliable data architectures and pipelines for model training, LLM fine-tuning, inference, and evaluation
Develop and maintain Airflow DAGs for metrics calculation, batch inference, KB workflows, and other data science projects
Collaborate with data scientists and engineers to integrate models and GenAI features into production systems Ensure high-quality code through best practices in testing, code reviews, documentation, and CI/CD for data and ML workloads
Work with tools and platforms like Airflow, Spark, Docker, Python Serverless, Kafka, and multiple cloud environments to deliver scalable, cost-effective GenAI solutions.
Job Description
As a data engineer in our data science group, youll build, use, and enhance our ML and GenAI platforms, powering data products and LLM-based services across. Youll design and operate data and MLOps infrastructure for batch inference, knowledge-base workflows, evaluation metrics, and fine-tuning pipelines. Collaborating with data scientists, product managers, and engineers, youll bring AI features into production at scale.
Extend and implement new capabilities in ML and GenAI platforms
Design and maintain scalable, reliable data architectures and pipelines for model training, LLM fine-tuning, inference, and evaluation
Develop and maintain Airflow DAGs for metrics calculation, batch inference, KB workflows, and other data science projects
Collaborate with data scientists and engineers to integrate models and GenAI features into production systems Ensure high-quality code through best practices in testing, code reviews, documentation, and CI/CD for data and ML workloads
Work with tools and platforms like Airflow, Spark, Docker, Python Serverless, Kafka, and multiple cloud environments to deliver scalable, cost-effective GenAI solutions.
Requirements:
Were looking for a technically proficient engineer with expertise in data, a passion for ML and GenAI, and the ability to identify and solve complex problems.
3+ years of experience in data engineering or building data-intensive systems
Proficient in Python and SQL with hands-on experience building data pipelines and production-grade services
Practical experience with Airflow, Spark, and Docker
Knowledge of or strong interest in data science and machine learning
Experience or strong interest in GenAI platforms and LLM-based services
Excellent communication and collaboration skills to work with cross-team stakeholders
Experience in cloud environments like AWS and/or GCP – an advantage.
Were looking for a technically proficient engineer with expertise in data, a passion for ML and GenAI, and the ability to identify and solve complex problems.
3+ years of experience in data engineering or building data-intensive systems
Proficient in Python and SQL with hands-on experience building data pipelines and production-grade services
Practical experience with Airflow, Spark, and Docker
Knowledge of or strong interest in data science and machine learning
Experience or strong interest in GenAI platforms and LLM-based services
Excellent communication and collaboration skills to work with cross-team stakeholders
Experience in cloud environments like AWS and/or GCP – an advantage.
This position is open to all candidates.
