Sr. Engineering Manager

As a seasoned professional with extensive experience in machine learning operations (MLOps), you will be responsible for leading a talented team and developing a comprehensive technical strategy. Your expertise in Python programming and ML frameworks such as TensorFlow or PyTorch will be crucial in implementing MLOps best practices, including model versioning, monitoring, automated deployment, and infrastructure automation. Your in-depth knowledge of Google Cloud Platform services, including Data Fusion, Dataproc, and Dataflow, will play a key role in data processing and pipeline orchestration. Experience with PostgreSQL databases and data integration tools like Qlik Replicate will further enhance your capabilities in this role. Security and privacy considerations for machine learning systems will be a top priority, requiring expertise in data encryption, access control, and compliance with regulations such as GDPR and HIPAA. Strong communication and leadership skills are essential for engaging both technical and non-technical stakeholders effectively. With a background in Computer Science, Engineering, or related field, along with 5+ years of experience managing software engineering or MLOps teams, you will be well-equipped to take on this challenging role. Hands-on experience deploying and managing machine learning models in production for 3+ years will further strengthen your candidacy. Your overall IT industry experience of 14+ years, along with relevant certifications in MLOps and Cloud platforms (especially GCP Professional Machine Learning Engineer or Data Engineer), will be valuable assets in this position. In this role, you will be leading the development and execution of a comprehensive technical strategy for the end-to-end ML lifecycle and experimentation capabilities. Your responsibilities will include fostering a culture of continuous learning and innovation, participating hands-on in coding, code reviews, troubleshooting, and mentoring, and creating scalable MLOps frameworks and infrastructure to support the full machine learning pipeline. Collaboration with data scientists, data engineers, software developers, and business stakeholders will be essential to ensure robustness, scalability, and performance in integrating ML models into production. Implementation of rigorous security best practices to maintain compliance with industry standards and regulations will be a critical aspect of your role. Furthermore, you will be responsible for maintaining thorough technical documentation, guidelines, and knowledge-sharing resources to support the ongoing success of the MLOps initiatives.,