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.

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About the Role

This role requires high-level autonomy, where you will define your own direction within strategic goals, influence multiple teams, and navigate high complexity and ambiguity to deliver tangible business results. You will bridge the gap between technical innovation and real-world business challenges by applying advanced statistical and machine learning techniques to build predictive models and derive actionable insights.

What we are looking for

• Experience & Education: Bachelor's degree in Computer Science or Engineering with 8–12 years of relevant experience leading complex technical projects with multi-team impact.
• Advanced ML & Statistics: Expert capability in designing end-to-end ML systems for complex problems (including imbalanced data and concept drift), applying advanced statistical methods (mixed models, survival analysis, Bayesian approaches), and demonstrating a deep understanding of causal inference techniques, experimentation design (A/B testing, MABs), and deep learning architectures for unstructured data (e.g., NLP, Computer Vision).
• Core Technology Stack: Expertise in Python (including libraries like Pandas and NumPy) or R, proficiency with cloud platforms and their data/ML services (e.g., AWS Sagemaker, GCP Vertex AI, Azure ML Services), experience with big data technologies (e.g., Spark, Hadoop), and familiarity with MLOps tools (e.g., Kubeflow, MLflow, Docker).
• Business Immersion: Proven ability to immerse in operations to acquire deep domain expertise, thinking like an insider to translate seamlessly between business needs and engineering solutions.
• Full-Stack Agility: Ability to build complete applications rapidly across any technology stack, selecting the right tools to balance technical debt with delivery speed.
• Data Strategy & Integration: Experience architecting scalable data strategies across diverse teams and complex enterprise landscapes. This includes defining data governance policies, mastering data quality frameworks, building robust integration solutions for undocumented schemas and disparate systems, and driving the adoption of modern data warehousing and lakehouse architectures.
• AI Evaluation & Governance: Experience leading AI governance initiatives, creating evaluation standards, and training teams on rigorous AI verification and risk management.
• Rapid Prototyping: Proficiency in leading rapid delivery initiatives using a prototype-first approach to validate solutions quickly before scaling.
• Problem Discovery: A track record of seeking out undefined problems, embedding with users to discover latent needs, and turning ambiguity into clear problem statements.
  
  

Who you are

• Polymath Oriented: You bridge gaps between engineering, design, business, and science, rapidly immersing yourself in new domains to speak the language of the business.
• Curious Explorer: You seek out ambiguity rather than avoiding it, driving team curiosity through challenging questions and creating an environment that encourages experimentation.
• Outcome Owner: You drive an accountability culture focused on business impact rather than just deliverables, owning relationships and making trade-offs between custom solutions and generalizable work.
• Systems Thinker: You map complex system interactions across technical and business domains, anticipating cascading effects and understanding how technology changes impact operations.
• Precise Communicator: You translate seamlessly between technical and business language, ensuring requirements are clear enough to enable AI generation and facilitate productive discussions with stakeholders.
• Problem Discoverer: You embed with users to discover latent needs, turning ambiguity into clear problem statements rather than waiting for defined tasks.

What Appnovation Offers 

• Challenging and rewarding work with real impact
• Direct Access to Cutting-Edge AI Platforms
• Diverse and Inclusive Culture 
• Growth opportunities for personal and professional development
• A collaborative and innovative work environment where your ideas are valued
• Exposure to exciting projects and high-profile clients
• Supportive work environment with access to company leaders
• Hybrid working model