Job Description
Job Description
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:
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.
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