Key Responsibilities:

• DataOps, MLOps, and AIOps
  • 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:
  Bachelor’s 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