Design, build, and optimize scalable, secure, and efficient data pipelines for AI/ML workflows.
Automate data ingestion, transformation, and deployment across AWS, GCP, and Azure.
Implement MLOps and AIOps for model versioning, monitoring, and automated retraining.
Ensure performance, security, scalability, and cost efficiency in AI lifecycle management.
Performance Optimization & Security
Monitor, troubleshoot, and optimize AI/ML pipelines and data workflows to enhance reliability.
Implement data governance policies, security best practices, and compliance standards.
Collaborate with cybersecurity teams to address vulnerabilities and ensure data protection.
Data Engineering & System Integration
Develop and manage real-time and batch data pipelines to support AI-driven applications.
Enable seamless integration of AI/ML solutions with enterprise systems, APIs, and external platforms.
Ensure data consistency, quality, and lineage tracking across the AI/ML ecosystem.
AI/ML Model Deployment & Optimization
Deploy and manage AI/ML models in production, ensuring accuracy, scalability, and efficiency.
Automate model retraining, performance monitoring, and drift detection for continuous improvement.
Optimize AI workloads for resource efficiency and cost-effectiveness on cloud platforms.
Continuous Learning & Innovation
Stay updated on AI/ML advancements, cloud technologies, and big data innovations.
Contribute to proof-of-concept projects, AI process improvements, and best practices.
Participate in internal research, knowledge-sharing, and AI governance discussions.
Cross-Functional Collaboration & Business Understanding
Work with business teams to ensure AI models align with organizational objectives.
Gain a basic understanding of how AI/ML supports predictive analytics, demand forecasting, automation, personalization, and content generation.
Qualifications:
Education:
Bachelors degree in Computer Science, Data Engineering, Information Technology, or a related field. Advanced degrees or relevant certifications (e.g., AWS Certified Data Analytics, Google Professional Data Engineer, Azure Data Engineer) are a plus.
Experience:
Minimum of 3-5 years experience in a data engineering or operations role, with a focus on DataOps, MLOps, or AIOps.
Proven experience managing cloud platforms (AWS, GCP, and/or Azure) in a production environment.
Hands-on experience with designing, operating, and optimizing data pipelines and AI/ML workflows.
Technical Skills:
Proficiency in scripting languages such as Python and Bash, along with experience using automation tools.
Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes) is desirable.
Strong knowledge of data processing frameworks (e.g., Apache Spark) and data pipeline automation tools.
Expertise in data warehouse solutions and emerging data lakehouse architectures.
Experience with AWS technologies is a plus, especially AWS Redshift and AWS SageMaker, as well as similar tools on other cloud platforms.
Understanding of machine learning model deployment and monitoring tools
