Job Summary

We are looking for a skilled Sr. Data Engineer - ML & AI Operations to join our growing team. In this role, you will be responsible for designing, developing, and deploying machine learning and generative AI (GenAI) platforms to deliver high-impact solutions to business challenges and optimize processes. This role focuses on the operationalization and automation of machine learning and AI solutions, ensuring they are seamlessly integrated into production environments with a high degree of scalability, reliability, and compliance with ethical guidelines.

The ideal candidate will bring strong technical expertise in data engineering, a deep understanding of ML and AI DevOps best practices, and a commitment to building robust, maintainable systems. If you are passionate about creating and operationalizing transformative ML and AI solutions, we’d love to hear from you!

Key Responsibilities

Platform Design & Development:

• Design, develop, and maintain scalable platforms for machine learning and GenAI, supporting end-to-end processes from data ingestion to model deployment and monitoring.

Model Deployment & Automation:

• Implement CI/CD pipelines for automated deployment of ML and AI models to production environments.
• Work closely with data scientists to ensure model readiness and optimization, focusing on robust deployment and monitoring.
• Develop and manage tools for continuous monitoring and performance management of models post-deployment to identify and resolve performance drift.

Collaboration and Business Alignment:

• Partner with data scientists, software engineers, product owners, and stakeholders to align ML and AI solutions with business goals and performance metrics.
• Facilitate seamless integration of ML/AI systems with business processes, ensuring data accessibility, quality, and real-time insights.

Operationalization & Maintenance:

• Ensure systems are built for scalability, maintainability, and security, adhering to best practices in ML & AI DevOps.
• Implement monitoring solutions to proactively address any issues in data, model performance, or infrastructure.

Ethics and Compliance:

• Integrate AI ethics and compliance considerations into all ML/AI solutions, with a focus on data privacy, bias detection, and model transparency.
• Implement processes to meet regulatory requirements and promote responsible AI use.

Education Requirements

• High school diploma or equivalent is required.
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Machine Learning, or a related field is preferred.

Qualifications and Requirements

Experience:

• 5+ years of experience in data engineering or DevOps roles, with a focus on ML/AI platforms and infrastructure.
• Proven experience in operationalizing and automating ML and GenAI solutions in production environments.
• Strong experience with cloud platforms (AWS, Azure, GCP) and managing infrastructure for data and machine learning systems.
   

Technical Skills:

• Proficiency in Python, SQL, and ML/AI DevOps tools (e.g., MLflow, Kubeflow, TensorFlow Extended).
• Experience with CI/CD tools (e.g., Jenkins, GitLab CI) and containerization/orchestration tools (Docker, Kubernetes).
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch) and data pipeline tools (e.g., Apache Airflow, dbt).

Key Skills and Competencies

• Technical Acumen: Strong knowledge of ML/AI lifecycle management, MLOps practices, and data pipeline optimization.
• Collaboration & Communication: Excellent teamwork skills with an ability to work closely with cross-functional teams and communicate complex technical concepts effectively.
• Problem-Solving: Proactive approach to identifying and solving issues in model performance, data quality, and infrastructure bottlenecks.
• Ethics and Compliance: Deep understanding of responsible AI practices, including bias detection, explainability, and data privacy.