Engineering Manager
2 weeks ago
**Responsibilities**:
**Leadership and Team Management**
- Lead, mentor, and manage a team of software engineers, fostering a collaborative and high
- performing culture.
- Set clear goals and expectations for team members and provide regular feedback on performance and professional development.
**Engineering and Project Management**
- Oversee the design, development, and deployment of software systems that utilize ML models, ensuring scalability, reliability, and maintainability.
- Collaborate with the Data Science team to understand ML model requirements and ensure seamless integration into production systems.
- Establish and manage engineering best practices, including code reviews, CI/CD pipelines, and software quality standards.
- Prioritize and manage multiple projects, ensuring timely delivery while maintaining a focus on quality and innovation.
**Collaboration and Communication**
- Act as a bridge between engineering, data science, and other departments to align technical solutions with business objectives.
- Communicate project progress, challenges, and outcomes to stakeholders at various levels of the organization.
- Foster a collaborative environment where cross-functional teams work effectively together.
**Technical Excellence**
- Stay updated on the latest trends and advancements in software engineering and machine learning.
- Drive technical innovation and advocate for the adoption of new tools and technologies to improve engineering efficiency.
- Troubleshoot and resolve technical issues as they arise, ensuring mínimal disruption to operations.
**Qualifications**:
**Required Skills and Experience**:
- Proven experience (5+ years) in software engineering with a strong focus on delivering production-grade services.
- At least 2+ years of experience in a leadership role, managing engineering teams.
- Hands-on experience with integrating and deploying ML models into production systems.
- Proficiency in programming languages such as Python, Java, Go, or similar.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Strong understanding of software architecture, microservices, and API development.
- Excellent communication, organizational, and project management skills.
**Preferred Skills**:
- Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch) and data processing pipelines.
- Experience in working with large-scale distributed systems.
- Knowledge of MLOps best practices and tools for managing the ML lifecycle (e.g., MLflow, Kubeflow).
- Background in data engineering, analytics, or distributed systems is a plus.
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