Lead Engineer

20 hours ago

Guindy National Park, India Prodapt Solutions Full time

Overview:

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

Responsibilities:

  • 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.

Requirements:

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.

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