Description

Job Description

• Strong programming skills, particularly in Python, with experience in ML frameworks like TensorFlow or PyTorch.
• Proven expertise in MLOps best practices including model versioning, monitoring, automated deployment, and infrastructure automation.
• In-depth knowledge of Google Cloud Platform services including Data Fusion, Dataproc, Dataflow for data processing and pipeline orchestration.
• Experience working with PostgreSQL databases and data integration tools like Qlik Replicate.
• Expertise in security and privacy considerations for machine learning systems, including data encryption, access control, and compliance with regulations such as GDPR and HIPAA.
• Strong communication and leadership skills, capable of engaging both technical and non-technical stakeholders effectively.

Responsibilities

You will develop and execute a comprehensive technical strategy encompassing architecture, design, implementation, and operation of end-to-end ML lifecycle and experimentation capabilities. Leading a high-performing team, you will take ownership of deliverables, foster a culture of continuous learning and innovation, and participate hands-on in coding, code reviews, troubleshooting, and mentoring. Your role will include creating and maintaining scalable MLOps frameworks and infrastructure to support the full machine learning pipeline-from data ingestion and processing through model deployment and monitoring.

You will collaborate closely with data scientists, data engineers, software developers, and business stakeholders to ensure robustness, scalability, and performance in integrating ML models into production. Implementing rigorous security best practices to maintain compliance with industry standards and regulations is essential. Additionally, you will maintain thorough technical documentation, guidelines, and knowledge-sharing resources.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.
• 5+ years managing software engineering or MLOps teams.
• 3+ years hands-on experience deploying and managing machine learning models in production.
• 14+ years overall IT industry experience.
• Relevant certifications in MLOps, Cloud platforms (especially GCP Professional Machine Learning Engineer or Data Engineer).