Hiring Data Engineer

Overview

Key Responsibilities:

• Data Pipeline Development:
• Design, build, and maintain scalable and efficient data pipelines for ingesting, processing, and transforming large datasets for AI/ML applications.
• Ensure data quality, integrity, and accessibility across the pipeline.
• AI/ML Model Integration:
• Collaborate with data scientists and ML engineers to operationalize AI/ML models into production environments.
• Implement model versioning, deployment strategies, and monitoring to ensure optimal performance.
• Cloud Infrastructure & SaaS Deployment:
• Architect, develop, and maintain cloud-based infrastructure (AWS, Azure, GCP, or similar) to support AI/ML workloads.
• Design and deploy AI/ML solutions as SaaS applications, ensuring scalability, reliability, and multi-tenancy.
• Automation & Monitoring:
• Develop CI/CD pipelines for deploying AI/ML models and data processing workflows.
• Implement monitoring and alerting systems to ensure system reliability and troubleshoot issues as they arise.
• Collaboration & Stakeholder Management:
• Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to define project requirements and deliverables.
• Provide technical guidance and mentoring to junior team members.
• Security & Compliance:
• Ensure adherence to data privacy regulations (e.g., GDPR, HIPAA) and best practices in securing cloud infrastructure and sensitive data.
• Implement robust access control and encryption mechanisms for data at rest and in transit.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field.
• 3+ years of experience in data engineering, with a focus on deploying cloud-based solutions.
• Proficiency in Python, SQL, and other programming languages commonly used in data engineering.
• Hands-on experience with cloud platforms (AWS, Azure, or GCP), particularly in AI/ML workloads.
• Expertise in building and managing ETL/ELT pipelines and big data frameworks (e.g., Apache Spark, Kafka).
• Familiarity with containerization and orchestration tools (Docker, Kubernetes).
• Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow).

Preferred Qualifications:

• Experience deploying AI/ML applications as SaaS products.
• Understanding of API development and microservices architecture.
• Experience with distributed systems and real-time data streaming.
• Familiarity with DevOps practices and infrastructure as code (e.g., Terraform, CloudFormation).

Job Type: Full-time

Pay: From ?25,000.00 per month

Schedule:

• Day shift

Experience:

• total work: 3 years (Required)

Work Location: In person

Application Deadline: 28/12/2024
Expected Start Date: 23/12/2024