Machine Learning Engineer II

Bengaluru - India

Monthly Salary: Not Disclosed

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

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 companys portfolio includes many of the worlds 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.

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.

Required Experience:

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.

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

Required Experience:

Key Skills

Industrial Maintenance
Machining
Mechanical Knowledge
CNC
Precision Measuring Instruments
Schematics
Maintenance
Hydraulics
Plastics Injection Molding
Programmable Logic Controllers
Manufacturing
Troubleshooting

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About Company

Condé Nast

Als exklusivstes Medienunternehmen der Welt zählt Condé Nast weltweit bekannte Marken wie VOGUE, GLAMOUR, GQ und AD Architectural Digest zum Portfolio.

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