Engineering Manager

Job not on LinkedIn

June 25

Apply Now

Receive Emails with Similar Jobs

Appier

WebsiteLinkedInAll Job Openings

Appier is a software-as-a-service (SaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier now has 17 offices across APAC, Europe and U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.

501 - 1000 employees

💰 $12.1M Venture Round on 2021-02

📋 Description

•Lead, mentor, and scale a team of engineers working across infrastructure, backend services, DevOps platforms, and MLOps •Collaborate with product, data, and machine learning teams to align engineering efforts with business goals •Oversee project planning, prioritization, and delivery with a focus on quality and velocity •Promote engineering excellence by establishing best practices in code quality, testing, deployment, and observability •Drive initiatives in system scalability, reliability, and maintainability •Contribute to infrastructure decisions, ensuring efficient CI/CD pipelines and DevOps tooling •(Preferred) Support and evolve MLOps pipelines in collaboration with ML engineers and data scientists

🎯 Requirements

•4+ years of hands-on software engineering experience, including at least 2+ years in a leadership or management role •Proven experience leading cross-functional technical teams in fast-paced environments •Solid understanding of DevOps principles and modern cloud infrastructure (e.g., AWS, GCP, Azure) •Familiarity with CI/CD tools (e.g., GitHub Actions, ArgoCD, Jenkins), infrastructure as code (e.g., Terraform, Pulumi), and container orchestration (e.g., Kubernetes) •Strong communication skills and the ability to collaborate effectively with both technical and non-technical stakeholders •Experience with MLOps workflows (model training, deployment, monitoring, data pipelines) •Background in machine learning infrastructure or working with ML engineering teams •Exposure to data platforms, versioning tools (e.g., DVC), or ML orchestration frameworks (e.g., Kubeflow, MLflow) •Ability to guide architectural decisions involving machine learning systems in production