Description

You will own the design, building, and maintenance of end-to-end MLOps pipelines.

Responsibilities

• Build and maintain scalable MLOps pipelines covering the full lifecycle: ingestion, training, versioning, and deployment.
• Deploy real-time ML models capable of sub-100ms latency on massive datasets.
• Detect and address data or model drift, automating necessary re-training workflows.
• Architect end-to-end AI/ML solutions, utilizing feature stores and model registries.
• Collaborate across data science, ML, and engineering teams to productionize models.

Required Skills

• 10+ years of experience in MLOps or Data Science.
• Proficiency in Python, Bash, Docker, and Kubernetes.
• Solid experience with streaming platforms like Apache Kafka, AWS Kinesis, or Spark Streaming.
• Experience with ML libraries including scikit-learn, TensorFlow, and PyTorch.
• Familiarity with core AWS services: S3, Lambda, CloudWatch, Step Functions, and Glue.
• Experience building and maintaining CI/CD pipelines for ML using Git, CodePipeline, or Jenkins.
• Hands-on experience with large-scale data processing (billions of records).
• Proficiency in Infrastructure as Code using Terraform or CloudFormation.

Key Skills

Education

Any Graduate