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We are looking for highly skilled and experienced professionals to join our team in two key roles: Machine Learning Engineer and ML Ops Engineer . As a Machine Learning Engineer , you will play a critical role in designing, developing, deploying, and optimizing machine learning models and intelligent applications that address complex business challenges. Working closely with data scientists, software engineers, and business stakeholders, you will bridge the gap between research and production by building scalable, reliable, and high-performing ML solutions across cloud and edge environments. As a Machine Learning Ops Engineer , you will serve as the engineering backbone of our AI delivery ecosystem, enabling Machine Learning Engineers working across Computer Vision and Generative AI initiatives to build, deploy, and operate production-grade ML systems at scale. Key Responsibilities of Machine Leaning Engineer Model Development & Implementation Design, develop, and implement machine learning models and algorithms to solve business problems Optimize model performance, accuracy, and efficiency through feature engineering and hyperparameter tuning Research and evaluate new ML techniques, frameworks, and tools Production Systems & Infrastructure Deploy ML models into production environments using cloud platforms and containerization technologies Build and maintain scalable ML pipelines for data processing, model training, and inference Implement MLOps practices including model versioning, monitoring, and automated retraining Ensure model reliability, scalability, and performance in production systems Optimize models for deployment across different environments (cloud, edge) Edge Deployment Deploy and optimize ML models for edge devices Implement model compression techniques (quantization, pruning, distillation) Work with edge computing frameworks and optimize for resource-constrained environments Ensure low-latency inference and efficient resource utilization Data Engineering & Analysis Work with large-scale datasets to extract, transform, and prepare data for ML applications Design efficient data pipelines Ensure data quality and implement data validation processes Collaboration & Communication Partner with data scientists, software engineers, and product teams to define requirements Communicate technical findings and model performance to both technical and non-technical stakeholders Participate in code reviews and maintain high standards for code quality Document models, processes, and best practices Key Responsibilities of ML Ops Engineer ML Pipeline Engineering Build and maintain reproducible ML pipelines for CV and GenAI use cases Convert notebook-based and ad hoc workflows into version-controlled, testable, maintainable pipeline components Standardize training and evaluation workflows across projects Improve data validation, artifact management, and clean handoffs between data preparation, training, and downstream delivery MLOps & Infrastructure Build and maintain CI/CD workflows for ML code, including automated testing and containerization Support ML development on AWS/Databricks, primarily SageMaker (training & pipelines), S3, and AI/ML experiment tooling Improve reproducibility through standardized experiment tracking, configuration management, and lineage tracking Evaluation & Model Quality Build standardized evaluation and validation harnesses — offline quality gates that every model passes before handoff Make model quality measurable, comparable, and regression-tested across iterations Define and own model performance and drift monitoring in close collaboration with the deployment team Cross-team Engineering & Standards Define shared ML engineering standards and reusable components used across projects Collaborate with ML algorithm engineers, data engineers, and the deployment team to negotiate clean interfaces and improve operational delivery Document workflows, assumptions, data/serving contracts, and operational procedures Requirements Qualifications & Experience: 3+ years in either machine learning engineering or software engineering and MLOps Strong Python, with a track record of building maintainable and testable ML / data pipeline code Hands-on experience with AWS services such as SageMaker, S3, or equivalent cloud ML infrastructure Experience with Docker and CI/CD workflows for ML or data applications Familiarity with experiment tracking, model versioning, and reproducible ML workflows Understanding of model training, evaluation, and validation workflows Strong communication and collaboration skills
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.