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Machine Learning Engineer II
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Condé Nast is a global media company producing the highest quality content with a footprint of more than 1 billion consumers in 32 territories through print, digital, video and social platforms. The company’s portfolio includes many of the world’s most respected and influential media properties including Vogue, Vanity Fair, Glamour, Self, GQ, The New Yorker, Condé Nast Traveler/Traveller, Allure, AD, Bon Appétit and Wired, among others.
Primary Responsibilities
Desired Skills & Qualifications
Preferred Qualifications
Job Description
Location:
Bengaluru, KA
About 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
- Design, build, and operate scalable, highly available ML systems for batch and real-time inference.
- Own the end-to-end production lifecycle of ML services, including deployment, monitoring, incident response, and performance optimization.
- Build and maintain AWS-native ML architectures using services such as EKS, SageMaker, API Gateway, Lambda, and DynamoDB.
- Develop and deploy low-latency ML inference services using FastAPI/Flask or gRPC, running on Kubernetes (EKS).
- Design autoscaling strategies (HPA/Karpenter), rollout mechanisms, traffic routing, and resource tuning for ML workloads.
- Engineer near-real-time data and inference pipelines processing large volumes of events and requests.
- Collaborate closely with Data Scientists to translate prototypes into robust, production-ready systems.
- Implement and maintain CI/CD pipelines for ML services and workflows using GitHub Actions and Infrastructure-as-Code.
- Improve observability, logging, alerting, and SLA/SLO adherence for critical ML systems.
- Follow agile engineering practices with a strong focus on code quality, testing, and incremental delivery.
Desired Skills & Qualifications
- 4-7+ years of experience in Machine Learning Engineering, MLOps, or Backend Engineering.
- Strong foundation in system design, distributed systems, and API-based service architectures.
- Proven experience deploying and operating production-grade ML systems on AWS.
- Strong proficiency in Python, with experience integrating ML frameworks such as PyTorch, TensorFlow, scikit-learn, and working with data processing libraries like Pandas, NumPy, and PySpark.
- Solid experience with AWS services, including (but not limited to): EC2, S3, API Gateway, Lambda, IAM, VPC Networking, DynamoDB.
- Hands-on experience building and operating containerized microservices using Docker and Kubernetes (preferably EKS).
- Experience building and deploying ML inference services, using:
- FastAPI / Flask / gRPC
- TorchServe, TensorFlow Serving, Triton, vLLM, or custom inference services
- Strong understanding of data structures, data modeling, and software architecture.
- Experience designing and managing CI/CD pipelines and Infrastructure-as-Code (Terraform) for ML systems.
- Strong debugging, performance optimization, and production troubleshooting skills.
- Excellent communication skills and ability to collaborate effectively across teams.
- Outstanding analytical and problem-solving skills.
- Undergraduate or Postgraduate degree in Computer Science or a related discipline.
Preferred Qualifications
- Experience with workflow orchestration and ML lifecycle tools such as Airflow, Astronomer, MLflow, or Kubeflow.
- Experience working with Databricks, Amazon SageMaker, or Spark-based ML pipelines in production environments.
- Familiarity with ML observability, monitoring, or feature management (e.g., model performance tracking, drift detection, feature stores).
- Experience designing or integrating vector search, embedding-based retrieval, or RAG-style systems in production is a plus.
- Prior experience operating low-latency or high-throughput services in a production environment.
What happens next?
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.
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