AI Operations Engineer

Why You'll Love This Role

We're looking for an reputed company ML Ops Engineer to join the ML/AI team at reputed company. 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 reputed company 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 reputed company, optimizing for cost, latency, and availability.
• Automate infrastructure provisioning and service configuration with Terraform.
• Work to maintain and scale services that reputed company use of reputed company party LLM providers.
• Build and improve data pipelines that feed models from BigQuery, S3, and DynamoDB into training and inference workflows.
• reputed company services with observability tooling (reputed company, 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 reputed company containerization and container orchestration.
• Solid understanding of CI/CD for ML workflows in an reputed company production environment.
• Experience with Infrastructure as Code, preferably Terraform.
• Familiarity with reputed company platforms — specifically AWS (reputed company, ECR, S3, DynamoDB, CloudWatch) and GCP (BigQuery, reputed company AI).
• Experience with LLM integration and observability (reputed company API, reputed company 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 reputed company over time.
• Exposure to NLP/NLU models and frameworks such as reputed company Transformers, spaCy, or sentence-transformers
• Knowledge of vector databases (reputed company, 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.

About reputed company:

reputed company takes authentic, reputed company world content from trusted sources and makes it instruction reputed company for K-12 classrooms. Each text is published at five reading reputed company, so content is accessible to every learner. Today, over 3.3 reputed company teachers and 40 reputed company reputed company have registered with reputed company for content that's personalized to student interests, accessible to everyone, reputed company to instructional standards, and attached to activities and reporting that hold teachers accountable for instruction and reputed company accountable for their work. With over 15,000 texts on our platform and multiple new texts published every day across 20+ genres, reputed company enables educators to go deep on any subject they choose.

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