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Contract: AI Operations Specialist

3 weeks, 1 day ago
Mexico, Argentina, Brazil, Chile, Colombia, Costa Rica
Contract
Mid Level
Machine Learning Engineer
DevOps and Infrastructure
AWS CI/CD Datadog Docker DynamoDB FastAPI GCP Generative AI LightGBM Machine Learning Microservices MLflow NLP OpenTelemetry Python PyTorch Scikit-learn spaCy TensorFlow Terraform Vertex AI XGBoost
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Newsela

Newsela

Newsela is an Instructional Content Platform that supercharges reading engagement and learning in every subject. They provide engaging, relevant instructional content for teachers that both educates and inspires. With a focus on meaningful classroom le...

Diversified Consumer Services
251-1K
Founded 2013
$172M raised
View All Jobs 24

Description

  • Design and maintain CI/CD pipelines for ML model training, packaging, and deployment across microservices.
  • Manage containerized services on AWS ECS with attention to cost, latency, and availability.
  • Automate infrastructure provisioning and service configuration using Terraform.
  • Maintain and scale services that integrate with third-party LLM providers.
  • Build and improve data pipelines that support training and inference workflows from BigQuery, S3, and DynamoDB.
  • Instrument services with Datadog, OpenTelemetry, and Langfuse, and establish SLOs for model-serving endpoints.
  • Collaborate with ML engineers to productionize new models using BentoML, FastAPI, and container-based serving.
  • Support ML/AI features, systems, and workflows in a hands-on production environment.

Requirements

  • 2-3 years of ML Ops experience supporting ML/AI features, systems, and workflows.
  • 3-4 years of prior experience in DevOps, CloudOps, or SRE.
  • Strong proficiency in Python.
  • Hands-on experience with Docker containerization and container orchestration.
  • Solid understanding of CI/CD for ML workflows in an enterprise production environment.
  • Experience with infrastructure as code, preferably Terraform.
  • Experience with AWS services including ECS, ECR, S3, DynamoDB, and CloudWatch.
  • Experience with GCP services including BigQuery and Vertex AI.
  • Experience with LLM integration and observability, such as OpenAI API, Google GenAI, and Langfuse tracing.
  • Experience building and maintaining data pipelines for ML training and feature engineering.
  • Familiarity with ML workflows including training, evaluation, experiment tracking, and model versioning.
  • Experience monitoring and flagging model drift over time.
  • Exposure to NLP/NLU models and frameworks such as Hugging Face Transformers, spaCy, or sentence-transformers.
  • Knowledge of vector databases and embedding-based retrieval systems such as LanceDB and FAISS.
  • Experience scaling and maintaining deep learning frameworks like TensorFlow and PyTorch in production.
  • Familiarity with classical ML libraries and explainability tools such as scikit-learn, XGBoost, LightGBM, and SHAP.
  • Working knowledge of ML serving frameworks such as BentoML or similar.
  • Comfort working with FastAPI or similar async Python web frameworks.

Benefits

  • This contract role is not eligible for company-sponsored benefits.
  • Remote work arrangement indicated by #LI-Remote.

Interested in this position?

Apply directly on the company website

Apply Now

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