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Evaluate and source appropriate cloud infrastructure solutions for machine learning needs, ensuring cost-effectiveness and scalability based on project requirements.Automate and manage the deployment of machine learning models into production environments, ensuring version control for models and datasets using tools like Docker and Kubernetes.Set up monitoring tools to track model performance and data drift, conduct regular maintenance, and implement updates for production models.Work closely with data scientists, software engineers, and stakeholders to align on project goals, facilitate knowledge sharing, and communicate findings and updates to cross-functional teams.Design, implement, and maintain scalable ML infrastructure, optimizing cloud and on-premise resources for training and inference.Document ML processes, pipelines, and best practices while preparing reports on model performance, resource utilization, and system issues.Provide training and support for team members on ML Ops tools and methodologies, and stay updated on industry trends and emerging technologies.Diagnose and resolve issues related to model performance, infrastructure, and data quality, implementing solutions to enhance model robustness and reliability.Education, Technical Skills & Other Critical Requirement: 3-6 years of relevant experience in AI/ analytics product & solution deliveryBachelor's/master's degree in an information technology/computer science/ Engineering or equivalent fields experience.Proficiency in frameworks such as TensorFlow, PyTorch, or Scikit-learn.Strong skills in Python and/or R; familiarity with Java, Scala, or Go is a plus.Experience with cloud services such as AWS, Azure, or Google Cloud Platform, particularly in ML services (e.g., AWS SageMaker, Azure ML).CI/CD tools (e.g., Jenkins, GitLab CI), containerization (e.g., Docker), and orchestration (e.g., Kubernetes).Experience with databases (SQL and NoSQL), data pipelines, ETL processes, ML pipeline orchestration (Airflow)Familiarity with monitoring and logging tools such as Prometheus, Grafana, or ELK stack.Proficient in using Git for version control.Strong analytical and troubleshooting abilities to diagnose and resolve issues effectively.Good communication skills for working with cross-functional teams and conveying technical concepts to non-technical stakeholders.Ability to manage multiple projects and prioritize tasks in a fast-paced environment. .
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.