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About The Role The role owns the infrastructure and pipelines that take machine learning models from experimentation to robust, scalable production environments. The team builds and maintains the core MLOps platform, ensuring reliable model serving, automated CI/CD workflows, and continuous monitoring for performance degradation and data drift. Key Responsibilities Architect, build, and maintain scalable ML infrastructure and deployment pipelines using Kubernetes, Docker, and Terraform Implement automated CI/CD workflows for machine learning applications, integrating unit testing, model validation, and security scans Optimize model serving architectures for low latency and high throughput using Triton Inference Server or Ray Serve Configure automated monitoring systems to detect data drift, concept drift, and resource bottlenecks across production models Collaborate with data scientists and ML engineers to streamline feature stores and artifact registries Write clean, infrastructure-as-code configurations and maintain comprehensive documentation for deployment processes What We Are Looking For 3–6 years of experience in MLOps, DevOps, or machine learning engineering with a strong focus on production infrastructure Demonstrated experience deploying and managing ML models in public cloud environments (AWS, GCP, or Azure) Strong proficiency in containerization and orchestration tools: Docker, Kubernetes, and Helm Hands-on experience with feature stores, model registries, and experiment tracking tools such as MLflow or Weights & Biases Solid software engineering fundamentals in Python, including scripting, API development, and automation Bonus: Experience with LLM serving infrastructure, vLLM, or distributed training frameworks like DeepSpeed
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.