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Job Title : Senior Machine Learning Engineer (MLOps Senior Engineer)Location : Chennai - 3 days WFOMust Have Skills : Azure, Docker, Terraform, Vertex AI, Google Cloud Platform, CKA (Certified Kubernetes Administrator), Ansible, FastAPI, AWS, Python, FlaskExperience Level : 6 - 9 YearsPrimary Skills : Python, Flask/Fast API, Docker, Kubernetes, Vertex AI, AWS, GCP, Azure.Shift Timing : Normal (Day Shift).Job Description Overview : We are seeking a highly skilled and experienced ML Ops Senior Engineer with a strong background in data engineering to join our dynamic team. The ideal candidate will be responsible for leading and implementing the deployment, monitoring, and management of machine learning models in production environments, while also possessing expertise in data engineering principles. This role requires a deep understanding of both machine learning and data engineering concepts, as well as proficiency in software engineering and DevOps practices. The ML Ops Senior Engineer will collaborate closely with data scientists, software engineers, and DevOps professionals to optimize the end-to-end ML pipeline, ensure model scalability, and maintain high availability of ML applications.Lead the design, development, and implementation of ML Ops solutions : Deploy machine learning models into production environments efficiently, leveraging data engineering best practices.Work with data engineers, data scientists, and software engineers to integrate data pipelines with ML models (including model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous training & deployment, and automated pipelines) with best practices, ensuring data quality, reliability, and scalability.Required Skillset:- Demonstrated expertise in building and managing MLOps pipelines using tools such as Kubeflow, MLflow, or SageMaker, with a deep understanding of containerization technologies like Docker and Kubernetes.- Proficiency in Python and strong experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn, coupled with a solid grasp of software engineering principles.- Ability to manage cloud infrastructure on platforms like AWS, Azure, or GCP, specifically focusing on serverless computing, distributed storage, and networking.- Strong analytical and problem-solving skills, with the ability to communicate complex technical concepts to non-technical stakeholders and collaborate effectively within a hybrid work environment in Chennai.- A Bachelor's or Master's degree in Computer Science, Data Science, or a related quantitative field, supported by 6 to 9 years of hands-on experience in production-level machine learning engineering.- Proven capability to mentor junior engineers and drive technical initiatives that improve team productivity and code quality. ( .
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.