Engineering Manager - Data (SDE 5)

Kredivo Group; Daerah Khusus Ibukota Jakarta; On-site; USD 30.000 - 50.000; Full time

Job summary

A leading technology company is looking for an experienced Engineering Manager to lead their Machine Learning Engineering team in Jakarta. This role involves mentoring engineers, overseeing software development leveraging ML models, and collaborating with various departments to drive innovation and quality. Candidates should have proven software engineering experience and a strong leadership background.

Qualifications

• 5+ years in software engineering, 2+ years in a leadership role.
• Proficient in Python, Java, Go, with experience in cloud platforms and containerization.
• Strong knowledge of software architecture and project management.

Responsibilities

• Lead and manage an ML engineering team, fostering collaboration.
• Oversee the design, development, and deployment of software integrating ML models.
• Establish engineering best practices and manage project timelines.

Skills

Software Engineering

Leadership

Project Management

Integration of ML Models

Python

Java

Cloud Platforms

Kubernetes

APIs

Tools

AWS

GCP

Azure

Docker

Job description

We are seeking an experienced and visionary Engineering Manager to lead our ML Engineering team within the Data Science Department. The successful candidate will be responsible for overseeing the development and delivery of robust and scalable software services that leverage Machine Learning (ML) models. As an Engineering Manager, you will collaborate closely with data scientists, product managers, and other stakeholders to ensure seamless integration of ML models into production systems, driving business impact and innovation.

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 minimal 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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