Why You'll Love This Role: 

We're looking for an experienced ML Ops Engineer to join the ML/AI team at Newsela. This team works on projects ranging from classical Machine Learning to AI / Generative pipelines.  This is a hands-on role. You'll work closely with ML/AI, data and site reliability engineers to take models from prototype to production, build robust data pipelines, and keep our services running smoothly as we continue to scale.

What You'll Be Doing:

• Design and maintain CI/CD pipelines for ML model training, packaging, and deployment across our microservices.
• Manage containerized services on AWS ECS, optimizing for cost, latency, and availability.
• Automate infrastructure provisioning and service configuration with Terraform.
• Work to maintain and scale services that make use of third party LLM providers.
• Build and improve data pipelines that feed models from BigQuery, S3, and DynamoDB into training and inference workflows.
• Instrument services with observability tooling (Datadog, OpenTelemetry, Langfuse) and establish SLOs for model-serving endpoints.
• Collaborate with ML engineers to productionize new models using BentoML, FastAPI, and container-based serving.

About You:

• 2-3 years in ML Ops supporting ML/AI features, systems and workflows with 3-4 years 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.
• Familiarity with cloud platforms — specifically AWS (ECS, ECR, S3, DynamoDB, CloudWatch) and GCP (BigQuery, Vertex AI).
• Experience with LLM integration and observability (OpenAI API, Google GenAI, Langfuse tracing).
• Experience building and maintaining data pipelines for ML training and feature engineering
• Familiarity with ML modeling workflows — training, evaluation, experiment tracking (e.g. MLFlow, Weights & Biases), 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 (LanceDB, FAISS) and embedding-based retrieval systems
• Experience with scaling and maintaining deep learning frameworks (TensorFlow, PyTorch) in production settings
• Familiarity with classical ML libraries (scikit-learn, XGBoost, LightGBM) and model explainability tools (SHAP)
• Working knowledge of ML serving frameworks such as BentoML or similar.
• Comfort working with FastAPI or similar async Python web frameworks.

Please note that given the nature of the contract, this role will not be eligible to participate in company-sponsored benefits

About Newsela:

Newsela is a leading education technology company dedicated to meaningful classroom learning for every student. We deliver integrated, AI-powered solutions designed to unlock student engagement, empower teachers, and drive meaningful learning outcomes. Our suite of products supports knowledge and skill development, writing practice, daily instruction, assessment, and data-informed decision-making across K–12 classrooms. Grounded in learning science research, Newsela’s solutions integrate content, assessment, and analytics to help educators track progress, understand student outcomes, and deliver high-impact instruction that supports every learner.

#LI-Remote

We are looking for a hands-on Senior Data Engineer to join a small team (2 Data Engineers + 1 Data Analyst). You will help scale our AWS-based data infrastructure, working primarily with industrial data sources and high-volume time-series data.

Responsibilities Will Include:  

• Engage directly with end users to understand how they consume data, and design solutions that serve their needs.
• Design, develop, and maintain scalable ETL/ELT pipelines for batch and real-time processing, including high-frequency time-series ingestion.
• Evolve the Data Lakehouse—optimizing storage, performance, and data consistency.
• Manage orchestration workflows with complex dependencies and error handling.
• Implement data governance, security controls, and audit policies across the AWS ecosystem, leveraging services such as AWS Lake Formation for centralized data lake permissions and governance.
• Optimize performance and cost (FinOps); identify and resolve technical bottlenecks.
• Partner with the Data Analyst to deliver production-ready, analytics-optimized datasets.
• Build monitoring, alerting, and data quality testing for platform reliability.

Requirements:

• Bachelor’s degree in Computer Science, Computer Engineering, or a closely related discipline is required.
• At a minimum, 5+ years of experience in Data Engineering, Analytics Engineering, or related roles.
• Deep hands-on experience with core AWS data services: Glue, Athena, Step Functions, Lambda, Kinesis, and Lake Formation.
• Hands-on experience with Apache Iceberg (required).
• Expert proficiency in Python (PySpark / Python Shell) and advanced SQL (window functions, CTEs, execution plan tuning).
• Solid grasp of ETL/ELT tooling and dimensional modeling best practices (Kimball).
• Experience preparing data architectures for visualization tools and operational/management reporting.
• Practical experience with NoSQL engines (e.g., DynamoDB) and relational SQL databases.
• Infrastructure as Code: Terraform or AWS CloudFormation.

Preferred Experience:

• Apache Airflow (MWAA experience preferred) or native AWS orchestration.
• Familiarity with dbt, Airbyte, or Fivetran.
• Experience automating CI/CD for Glue jobs or Lambda deployments.

Soft Skills:

• Senior peer mindset—leads through code reviews and mentorship, not hierarchy.
• Pragmatic decision-maker who picks the right tool for cost, efficiency, and project needs.
• Strong ownership of end-to-end data quality and system availability.
• Clear communicator who can translate business needs into technical solutions.