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Engineer/ML Ops Key Responsibilities 1. Assist in the design and development of MLOps pipelines for deployment and integration of ML models. 2. Support data scientists and senior engineers in operationalizing machine learning models. 3. Automate model training, testing, and deployment workflows using Python and shell scripting. 4. Monitor models in production and flag performance issues or anomalies for resolution. 5. Implement and maintain version control practices for ML models and data. 6. Containerize ML services and applications using Docker. 7. Set up and maintain Jenkins pipelines for continuous integration and delivery. 8. Support Kubernetes-based deployment of ML workloads. 9. Ensure adherence to security, data privacy, and governance standards. 10. Document MLOps processes, workflows, and configurations for team knowledge sharing. 11. Stay current with emerging MLOps tools, frameworks, and best practices. 12. Participate in sprint planning, standups, and retrospectives in an Agile environment. Skills 1. Hands-on experience or strong familiarity with MLFlow. 2. Proficiency in Python; experience with automation and Shell/Bash scripting. 3. Working knowledge of Docker for containerization. 4. Experience with Jenkins or similar CI/CD tools. 5. Basic understanding of Kubernetes for container orchestration. 6. Understanding of machine learning concepts and the model development lifecycle. 7. Familiarity with SDLC practices and Agile methodologies. 8. Good analytical and problem-solving skills. 9. Strong communication skills and ability to work collaboratively in a team. 10. Bachelor's degree in Computer Science, Data Science, Information Technology, or a related field. .
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.