Machine Learning Operations Engineer
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Tech stack
Job description
Integral Ad Science (IAS) is a global technology and ad measurement company that builds verification, optimization, and analytics solutions for the advertising industry. We are looking for a Senior Machine Learning Operations Engineer to join our Research and Development (R&D) team. If you are excited by technology that has the power to handle hundreds of thousands of transactions per second; collect tens of billions of events each day; and evaluate thousands of data points in real time, all while responding in just a few milliseconds, then IAS is the place for you!
As a Senior Machine Learning Operations Engineer, you will be responsible for designing, implementing, and optimizing machine learning release pipelines and scalable data processing systems, while also building and maintaining big data visualizations for production monitoring. Positioned at the intersection of data science machine learning team, software engineering, and cloud infrastructure, you will collaborate across teams to ensure robust deployments, high-quality standards, and cost-efficient solutions at scale. You will also contribute to roadmap building, production monitoring, KPIs definition, and technical mentoring. As a key member of the team, your technical expertises in combination with your strong knowledge of agile methodologies will be key to unlock our next challenges.
What you'll get to do:
- Optimize release pipelines: build, maintain, and enhance scalable release pipelines for machine learning and data science products, ensuring smooth transitions from development to production in close collaboration with ML engineers.
- Data visualization & monitoring: create and maintain dashboards (Grafana and other technologies) to visualize large-scale data flows and model performance, ensuring continuous monitoring of production systems at scale for our clients.
- Scale systems: refine implementation of cost-effective large-scale multimedia and data processing pipelines on cloud and GPU-enabled infrastructures.
- Enforce best practices: power advanced release management solutions with strong testing procedures : canary deployments, validation testing, ML QA automation.
- Cross-functional collaboration: work closely with data scientists, backend developers, DevOps engineers, and stakeholders to document, refine, and oversee the release lifecycle.
- Mentorship & leadership: mentor engineers and promote engineering rigor, agile practices, and technical excellence across teams.
- Drive cost efficiency: continuously optimize the scalability and cost-effectiveness of our release and infrastructure ecosystem, improving ROI and resource utilization.
- Innovate & experiment: explore and integrate emerging frameworks to improve the ML lifecycle & accelerate adoption of emerging technologies such as LLMs & foundation models., First Name * Last Name * Email * Phone *
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Requirements
- 4+ years of professional experience as a backend or full-stack engineer, with exposure to data pipelines and large-scale systems maintaining robust release processes for big data pipelines or AI/ML-based services.
- Strong expertise in Python as well as solid background in Java, JavaScript, and SQL.
- Deep understanding of cloud environments for the AI world, including custom integrations between cloud-based systems.
- Hands-on experience maintaining complex databases such as DataBricks DeltaLike or equivalent and optimizing queries at scale.
- Experience developing web backends and UIs, including interactive components for data visualization and dashboards.
- Familiarity with CI/CD practices and software release management (testing, validation, automated deployments).
- Strong knowledge of system optimization and cross-language development environments.
- Comfortable collaborating in agile environments, documenting processes, and working with diverse technical teams.
What puts you over the top:
- Experience with machine learning lifecycle tools (MLflow, Triton Inference Server…).
- Experience with Databricks or other extract, transform, load (ETL) big data pipelines.
- Hands-on experience with data visualization and monitoring tools such as Grafana.
- Exposure to cloud computing environments (AWS / GCP) and containerized deployments (Docker, Kubernetes).
- Strong background in ML oriented QA automation, functional testing, and system validation.
- Previous experience in AI / DeepLearning model development.
- Passion for solving complex problems, mentoring peers, and continuously learning new technologies.
