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60 Tecton Jobs Hiring Near You

AppRecode, Inc.

NY · On-site

$120 - $160/hr

MLflow, Weights & Biases, DVC, Feature stores (Feast, Tecton), Model monitoring (Evidently, WhyLabs), Apache Airflow, Spark, Ray, Helm, ArgoCD, Prometheus, Grafana, Data versioning, A/B testing for ...

Parafin

Senior Software Engineer, Data Platform

$125K - $165K/yr

Feature stores (e.g., Tecton), ML model lifecycle (training, deployment, monitoring, retraining), real-time inference. • Contributions to internal tooling or open-source projects in the data ...

Lumen Solutions

AI/ML Engineer

Austin, TX · On-site +1

Production CV experience with PyTorch/TensorFlow, OpenCV, YOLO, object detection, segmentation, or real-time inference.3RequiredFeature stores (Feast, Tecton) or advanced feature engineering ...

Bravens

Specialist

Austin, TX · On-site

Feature engineering and feature stores (Feast, Tecton). * Model optimization techniques including quantization, pruning, and knowledge distillation. * Experience with Ollama, Hugging Face, or other ...

BizFirst

MLOps Engineer

Alexandria, VA · On-site

$140 - $200/hr

Preferred Familiaritywith feature stores (Feast, Tecton, or similar) and online/offline featureserving patterns. Backgroundworking in a fast-moving team where data scientists and ML engineers ...

New

LanceDB

Senior Solutions Engineer

San Francisco, CA

$65 - $84/hr

Have worked with feature stores (e.g., Feast, Tecton) or have designed custom ML feature pipelines. * Have experience in observability and monitoring (Prometheus, Grafana, ELK/EFK). * Are familiar ...

LanceDB Inc

Senior Solutions Engineer

San Francisco, CA · On-site

$180K - $250K/yr

Have worked with feature stores (e.g., Feast, Tecton) or have designed custom ML feature pipelines. * Have experience in observability and monitoring (Prometheus, Grafana, ELK/EFK). * Are familiar ...

LanceDB Inc.

Senior Solutions Engineer

San Francisco, CA · On-site

$140 - $210/hr

Have worked with feature stores (e.g., Feast, Tecton) or have designed custom ML feature pipelines. * Have experience in observability and monitoring (Prometheus, Grafana, ELK/EFK). * Are familiar ...

LanceDB

Senior Solutions Engineer

San Francisco, CA

$65 - $84/hr

Have worked with feature stores (e.g., Feast, Tecton) or have designed custom ML feature pipelines. * Have experience in observability and monitoring (Prometheus, Grafana, ELK/EFK). * Are familiar ...

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Senior MLOps Engineer

AppRecode, Inc.

NY • On-site

$120 - $160/hr

Other

Posted 28 days ago

Job description

About the Position

The client is focused on improving and scaling machine learning systems. They need a senior MLOps engineer to build end‑to‑end ML pipelines in the cloud, automate model training and deployment, and ensure production ML systems are monitored, reliable, and scalable.

Start: December 1, 2025

Key Responsibilities
  • Automate machine learning model training and deployment processes using CI/CD pipelines
  • Build end-to-end MLOps pipelines in cloud platforms (AWS / GCP / Azure)
  • Implement monitoring and observability for ML models in production environments
  • Optimize infrastructure for ML workloads to improve reliability, scalability, and efficiency
  • Deploy and manage containerized ML applications using Docker and Kubernetes
  • Implement model versioning, experiment tracking, and model registry solutions
  • Set up data pipelines and feature stores for ML model training
  • Ensure ML model performance monitoring, drift detection, and retraining automation
  • Collaborate with data scientists to operationalize ML models from development to production
  • Implement infrastructure as code for ML infrastructure using Terraform or similar tools

Reports to: Client’s Engineering Manager / CTO

Collaborates with: Data Science team, Engineering teams, DevOps team

Technologies

Must-have: MLOps practices, CI/CD for ML (GitHub Actions, GitLab CI, Azure DevOps), Docker, Kubernetes, Cloud platforms (AWS / GCP / Azure), Python, Infrastructure as Code (Terraform), ML frameworks (TensorFlow, PyTorch, scikit-learn), Model deployment (SageMaker, Vertex AI, Azure ML, or Kubeflow)

Nice-to-have: MLflow, Weights & Biases, DVC, Feature stores (Feast, Tecton), Model monitoring (Evidently, WhyLabs), Apache Airflow, Spark, Ray, Helm, ArgoCD, Prometheus, Grafana, Data versioning, A/B testing for models

Soft Skills
  • Fluent English (conversational and written)
  • Strong problem-solving and analytical skills
  • Ability to work independently and implement ML processes end-to-end
  • Collaboration skills working with data scientists and engineers
  • Understanding of ML model lifecycle from data to production
  • Highly self‑managed and able to plan, estimate, and execute tasks
Challenges & Milestones

First 90 Days: Assess current ML infrastructure, implement initial MLOps automation, set up model monitoring for production models

Months 3-6: Build end-to-end ML pipelines with automated training and deployment, implement experiment tracking and model registry, optimize infrastructure costs

Months 6-12: Full MLOps platform operational with automated retraining, drift detection, A/B testing capabilities, and scalable infrastructure supporting multiple ML models

Working Hours

Full-time (40 hours/week), Remote

Flexible hours with reasonable overlap for team collaboration

We are seeking a Senior MLOps Engineer to build and scale machine learning systems in the cloud. This role focuses on automating ML model training, deployment, and monitoring to ensure reliable production ML operations.

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