MLOps (Machine Learning Operations) Tech Lead

Henkel • Cleaning & Household Products • Chemical Manufacturing • 6 days ago
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Salary
≈ $88k – $213k per year (Estimated)
Location
In office (Cairo)
Seniority
Staff

Confirmed on the employer's own hiring board on Oct 17, 2026. First seen by Alion on Jul 10, 2026. Henkel scores A on the Alion truth index.

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Henkel

Henkel is a German multinational chemical and consumer goods company headquartered in Düsseldorf, organised into two business areas, Adhesive Technologies and Consumer Brands, with brands such as Loctite, Persil and Schwarzkopf. Founded in 1876 and listed in the DAX, it operates worldwide, with its North American headquarters in Rocky Hill, Connecticut, and large teams in Stamford, Boulogne-Billancourt, Shanghai, Brussels and Bratislava. Its vacancies include field sales and sector managers, production planners, quality engineers, engineering project managers, R&D and packaging interns, enterprise architects, logistics managers and legal counsel.
henkel.com • HQ: Düsseldorf, Germany • Hair Care Products • Cleaning & Household Products • FMCG & Consumer Brand Groups • Chemical Manufacturing • 1001-5000 employees • Est. 1876
Tech stack of Henkel

About this Position Join our team and help accelerate AI adoption across the organization by building the platforms, tools, and engineering practices that power AI at scale. What you´ll do Design, build, and maintain end-to-end MLOps and AI engineering solutions, enabling efficient model development, deployment, monitoring, and lifecycle management. Develop and optimize data and ML pipelines using modern cloud and orchestration technologies.

Implement automation, CI/CD pipelines, infrastructure-as-code, and reusable engineering frameworks to improve reliability, consistency, and time-to-market. Establish and promote best practices, standards, and governance mechanisms for AI engineering and model operations. Collaborate closely with data scientists, data engineers, architects, and business stakeholders to bring AI solutions from experimentation into production.

Monitor, troubleshoot, and continuously improve the performance, scalability, and cost efficiency of AI systems and infrastructure. Evaluate and adopt emerging technologies in AI, MLOps, and cloud engineering to drive innovation and operational excellence.

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