Location:
Bengaluru, KAAbout The Role:
Condé Nast is seeking a motivated and skilled Machine Learning Engineer I to support the productionization of machine learning projects in Databricks or AWS environments for the Data Science team.
This role is ideal for an engineer with a strong foundation in software development, data engineering, and machine learning, who enjoys transforming data science prototypes into scalable, reliable production pipelines.
Note: This role focuses on deploying, optimizing, and operating ML models rather than building or researching new machine learning models.
Primary Responsibilities
Build, optimize, and maintain data and ML pipelines to deploy machine learning models into production environments.
Assist in transforming data science prototypes into reusable, production-ready engineering frameworks.
Contribute to the design and implementation of scalable ML workflows processing large volumes of data.
Support near-real-time and batch processing systems for ML use cases.
Collaborate closely with Machine Learning Engineers and Data Scientists in designing and engineering ML solutions.
Participate in the full development lifecycle, from design and implementation to testing and release.
Implement and maintain CI/CD pipelines for ML models and data workflows.
Proactively identify, debug, and resolve issues in ML pipelines and production jobs.
Follow agile development practices with a focus on code quality, testing, and incremental delivery.
Participate in quality assurance, testing, and defect resolution.
Desired Skills & Qualifications
2-4 years of software development experience involving machine learning or data-intensive systems.
Strong proficiency in Python, with experience using libraries such as TensorFlow, PyTorch, scikit-learn, Pandas, NumPy, and PySpark.
Good understanding of data structures, data modeling, and software engineering principles.
Experience working with big data technologies such as Spark, Hadoop, Kafka, Hive, or AWS EMR.
Exposure to Databricks or Amazon SageMaker for ML development or deployment.
Experience building data pipelines and ML workflows in production or pre-production environments.
Familiarity with API development and serving ML models as RESTful services.
Experience working with Docker and basic exposure to Kubernetes is a plus.
Experience with CI/CD pipelines for ML or data workflows.
Good communication skills and ability to work effectively within a team.
Strong analytical and problem-solving skills.
Undergraduate or Postgraduate degree in Computer Science or a related discipline.
Preferred Qualifications
Experience using Airflow, Astronomer, MLflow, or Kubeflow.
Exposure to Spark, or PySpark in data processing systems.
Familiarity with AWS services commonly used in ML pipelines (S3, EC2, IAM, etc.).
Experience with near-real-time data processing use cases.
If you are interested in this opportunity, please apply below, and we will review your application as soon as possible. You can update your resume or upload a cover letter at any time by accessing your candidate profile.
Condé Nast is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, age, familial status and other legally protected characteristics.
Top Skills
What We Do
Condé Nast is a global media company, home to iconic brands including Vogue, The New Yorker, GQ, Glamour, AD, Vanity Fair and Wired, among many others. The company's award-winning content reaches 88 million consumers in print, 419 million in digital and 432 million across social platforms, and generates more than 1 billion video views each month.
The company is headquartered in New York and London, and operates in 32 markets worldwide including China, France, Germany, India, Italy, Japan, Mexico and Latin America, Russia, Spain and Taiwan. Launched in 2011, Condé Nast Entertainment is an award-winning production and distribution studio that creates programming across film, television, social and digital video and virtual reality.
