Technical Lead/Coordinator (ML Ops/DevOps)

The role of warehousing and logistics systems is becoming increasingly crucial in enhancing the competitiveness of various companies and contributing to the overall efficiency of the global economy. Modern intra-logistics solutions integrate cutting-edge mechatronics, sophisticated software, advanced robotics, computational perception, and AI algorithms to ensure high throughput and streamlined processing for critical commercial logistics functions. Our Warehouse Execution Software is designed to optimize intralogistics and warehouse automation by utilizing advanced optimization techniques. By synchronizing discrete logistics processes, we have created a real-time decision engine that maximizes labor and equipment efficiency. Our software empowers customers with operational agility essential for meeting the demands of an Omni-channel environment. We are seeking a dynamic individual who can develop state-of-the-art MLOps and DevOps frameworks for AI model deployment. The ideal candidate should possess expertise in cloud technologies, deployment architectures, and software production standards. Moreover, effective collaboration within interdisciplinary teams is key to successfully guiding products through the development cycle. **Core Job Responsibilities:** - Develop comprehensive pipelines covering the ML lifecycle from data ingestion to model evaluation. - Collaborate with AI scientists to expedite the operationalization of ML algorithms. - Establish CI/CD/CT pipelines for ML algorithms. - Implement model deployment both in cloud and on-premises edge environments. - Lead a team of DevOps/MLOps engineers. - Stay updated on new tools, technologies, and industry best practices. **Key Qualifications:** - Master's degree in Computer Science, Software Engineering, or a related field. - Proficiency in Cloud Platforms, particularly GCP, and relevant skills like Docker, Kubernetes, and edge computing. - Familiarity with task orchestration tools such as MLflow, Kubeflow, Airflow, Vertex AI, and Azure ML. - Strong programming skills, preferably in Python. - Robust DevOps expertise including Linux/Unix, testing, automation, Git, and build tools. - Knowledge of data engineering tools like Beam, Spark, Pandas, SQL, and GCP Dataflow is advantageous. - Minimum 5 years of experience in relevant fields, including academic exposure. - At least 3 years of experience in managing a DevOps/MLOps team.,