Engineering Manager
1 week ago
Engineering Manager (Python & AI)
Role Overview
We are looking for an Engineering Manager with years of experience to lead a team responsible for building scalable AI-driven systems in Python. The role involves team leadership, system development, and hands-on contributions in areas such as server management, CI/CD automation, scraping, ETL processes, and AI tools/model development.
Key Responsibilities
Team & Project Management
Lead and manage a team of Python engineers.
- Plan, assign, and monitor tasks to ensure timely project delivery.
Conduct code reviews and enforce coding standards.
System Development & Operations
Oversee development and deployment of server-based applications.
- Design and implement CI/CD pipelines and automation workflows.
Manage infrastructure for reliable and scalable AI systems.
Data Engineering & AI
Develop and maintain web scraping pipelines for structured/unstructured data collection.
- Design and manage ETL workflows for data ingestion and transformation.
Guide development, training, and deployment of AI/ML models.
Quality & Performance
Ensure adherence to best practices in testing, monitoring, and version control.
- Optimize system performance, scalability, and maintainability.
- Collaborate with stakeholders to align technical execution with business needs.
Required Skills & Experience
- Experience: years in software engineering, including 3+ years in a leadership role.
- Programming: Strong proficiency in Python and its libraries/frameworks.
Server & DevOps:
Experience with server-side development.
Hands-on expertise in CI/CD pipelines and automation (Jenkins, GitHub Actions, GitLab CI, etc.).
Data & AI:
Proficiency in scraping frameworks (Scrapy, Selenium, etc.).
- Experience in ETL pipelines and data processing.
Knowledge of AI/ML frameworks (PyTorch, TensorFlow, scikit-learn).
Other:
Familiarity with cloud platforms (AWS, GCP, Azure).
- Strong understanding of system design and distributed systems.
Preferred Skills
- Knowledge of MLOps tools for deploying, monitoring, and scaling ML models
- Experience with containerization (Docker, Kubernetes)
- Exposure to microservices architecture
- SaaS-specific scaling and performance optimization
- Familiarity with security practices for SaaS AI systems
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