Lead Engineer (AI/ML - Simulation Pod) bei Prodapt

Join the Prodapt team in building AI/ML Solutions. If you have 5 - 10 years of experience & would like to work on a cutting-edge ML platform, enabling efficient analysis, testing, and decision-making at scale.

• Design, develop, and maintain simulation services and tools for ML feature, model, and rule evaluation. 

• Build and optimize data pipelines for point-in-time historical data access and feature backfilling. 

• Containerize and deploy ML models and services using Docker and Seldon on Kubernetes clusters. 

• Integrate with Client’s cloud infrastructure (GCP) and internal APIs for seamless simulation workflows. 

• Collaborate with data scientists, ML engineers, and QA to deliver robust, scalable solutions. 

• Contribute to CI/CD pipelines and DevOps practices for automated testing, deployment, and monitoring. 

• Write clean, maintainable, and well-documented code using Python and related SDKs. 

• Participate in code reviews, design discussions, and platform architecture decisions. 

• Troubleshoot, debug, and optimize simulation jobs and platform services. 

Required Qualifications 

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. 

• Proficiency in Python and experience with ML/AI model development. 

• Hands-on experience with Docker for containerization and Seldon for model deployment. 

• Familiarity with Kubernetes for orchestration and cloud platforms (preferably GCP). 

• Experience with Git for version control and collaborative development. 

• Strong understanding of APIs, microservices, and distributed systems. 

• Experience with CI/CD and DevOps tools for ML workflows. 

• Ability to work in a fast-paced, collaborative environment and communicate effectively. 

Preferred Qualifications 

• Experience with feature engineering, backfilling, and point-in-time data access. 

• Familiarity with  large-scale financial/ML platforms. 

• Experience with Jupyter Notebooks and AI SDKs. 

• Knowledge of MLOps best practices, including monitoring, model versioning, and automation. 

• Exposure to big data technologies and scalable data processing.