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Tecton Jobs (NOW HIRING)

ML OPS ARCHITECT

Woodland Hills, CA · On-site

$67.50 - $86.75/hr

Tecton, Databricks, FeatureForm). • 3+ years: DevOps (Eg. Argo CD / Argo Workflows), Containerization (Kubernetes, ROSA). • 3+ years: Enterprise Application Integration (Eg. Guidewire, Salesforce ...

Preferred QualificationsAWS Certified Machine Learning - SpecialtyExperience with feature stores, model registries, and monitoring tools such as MLflow, Tecton, or Seldon.Familiarity with data ...

Artificial Intelligence Engineer (AI/ML)

Austin, TX · On-site

$113.50K - $136.30K/yr

Go or Rust experience for performance-critical components Feature stores (Feast, Tecton) or advanced feature engineering Model optimization: quantization, pruning, knowledge distillation Edge ...

Senior ML Data Engineer (P508)

Chicago, IL · On-site

$109.30K - $148.50K/yr

... Tecton, or similar enterprise solutions • Deep knowledge of point-in-time correctness principles, temporal joins, and time-series data modeling best practices • Multi-cloud experience with both ...

Senior ML Data Engineer (P508)

Chicago, IL · On-site

$109.30K - $148.50K/yr

... Tecton, or similar enterprise solutions • Deep knowledge of point-in-time correctness principles, temporal joins, and time-series data modeling best practices • Multi-cloud experience with both ...

Senior ML Data Engineer (P508)

Cincinnati, OH · On-site

$103.40K - $140.50K/yr

... Tecton, or similar enterprise solutions • Deep knowledge of point-in-time correctness principles, temporal joins, and time-series data modeling best practices • Multi-cloud experience with both ...

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Tecton information

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How much do tecton jobs pay per hour?

As of Jun 4, 2026, the average hourly pay for tecton in the United States is $26.34, according to ZipRecruiter salary data. Most workers in this role earn between $15.14 and $30.77 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Platform Engineer at Tecton, and why are they important?

To excel as a Machine Learning Platform Engineer at Tecton, you need a solid background in computer science, data engineering, and machine learning, often demonstrated by a relevant degree and prior experience building data infrastructure. Familiarity with tools like Python, SQL, cloud platforms (AWS, GCP, Azure), and technologies such as Apache Spark or Kubernetes is typically required. Strong problem-solving abilities, collaboration, and effective communication help you work with cross-functional teams and address complex engineering challenges. These skills are vital for designing scalable systems that enable efficient development and deployment of machine learning models.

What are the most common challenges faced by Machine Learning Engineers working with Tecton in deploying real-time features?

Machine Learning Engineers using Tecton often encounter challenges related to integrating real-time feature pipelines with existing data infrastructure and ensuring low-latency performance. Managing data quality, monitoring feature freshness, and coordinating deployments across teams can be complex, especially as models scale to production. Close collaboration with data engineers and DevOps teams is essential for maintaining robust, automated data pipelines and troubleshooting issues quickly.

What are Tecton engineers?

Tecton engineers are professionals who specialize in building and managing feature platforms for machine learning applications. They work with data pipelines, infrastructure, and tools to ensure high-quality, real-time, and batch feature data is accessible for ML models. Tecton engineers often collaborate with data scientists and ML engineers to streamline the process of developing, deploying, and monitoring machine learning features, enabling faster and more reliable AI solutions.

What is the difference between Tecton vs Data Engineer?

AspectTectonData Engineer
Primary RoleBuilds and manages feature stores for machine learning modelsDesigns, develops, and maintains data pipelines and infrastructure
Skills & CertificationsMachine learning, data engineering, cloud platforms, SQLData pipeline tools, SQL, Python, cloud services
Work EnvironmentCollaborates with data scientists and ML teamsWorks with data engineers, analysts, and software teams
Industry UsageUsed in organizations deploying ML modelsUsed across data-driven companies for data infrastructure

While both Tecton and Data Engineers work with data infrastructure, Tecton specializes in building feature stores for machine learning applications, whereas Data Engineers focus on creating data pipelines and managing data infrastructure for various business needs. The roles often overlap but serve different core functions within data teams.

More about Tecton jobs
What states have the most Tecton jobs? States with the most job openings for Tecton jobs include:
Infographic showing various Tecton job openings in the United States as of May 2026, with employment types broken down into 94% Full Time, and 6% Contract. Highlights an 83% Physical, 7% Hybrid, and 10% Remote job distribution, with an average salary of $54,791 per year, or $26.3 per hour.

ML OPS ARCHITECT

Purple Drive Technologies

Woodland Hills, CA • On-site

$67.50 - $86.75/hr

Full-time

Posted 20 days ago


Job description

Overview:
Description:
"• Architect and implement scalable AWS ML/AI cloud infrastructure in a multi-tenant SaaS environment.
• Collaborate with data scientists, data engineers, and IT teams to define requirements and best practices for ML model development, deployment, and monitoring.
• Evaluate and recommend tools, platforms, and cloud technologies for ML Ops, ensuring alignment with enterprise architecture standards.
• Oversee the integration of ML pipelines with existing enterprise data and application architectures. Familiarity with Guidewire integrations is highly desirable.
• Oversee ML/AI related Kubernetes cluster management and provide guidance on alternative ML/AI workflow orchestration options such as Argo vs Kubeflow, and ML/AI data pipeline creation, management and governance with tools like Airflow.
• Employ tools like Argo CD to automate infrastructure deployment and management.
• Mentor and guide technical teams on ML Ops architecture, tooling, and best practices."
"• 5+ years: AI/ML Strategy & Roadmap Development.
• 4+ years: MLOps Tools (Eg. AWS Sagemaker, GCP Vertex AI, Databricks).
• 3+ years: ML & Data Pipeline Orchestration (Eg. Kubeflow, Apache Airflow).
• 2+ years: ML Feature Store Tools (Eg. Tecton, Databricks, FeatureForm).
• 3+ years: DevOps (Eg. Argo CD / Argo Workflows), Containerization (Kubernetes, ROSA).
• 3+ years: Enterprise Application Integration (Eg. Guidewire, Salesforce).
• 4+ years: Data Platforms (Eg. Snowflake, RedShift, BigQuery).
• 2+ years: GenAI Tools / LLMs (Eg. OpenAI, Gemini, etc.).
• 1+ year: Agentic AI Frameworks (Eg. LangGraph, Autogen, Google ADK).
• 3+ years: API Orchestration (Eg. Mulesoft, Google Cloud API).
Architecture Experience Required
• 3+ years: Data Mesh Architecture & Data Product Design.
• 3+ years: Event-Driven Architecture (EDA).
• 4+ years: Scalable AWS ML/AI Cloud Infrastructure (Multi-tenant SaaS).
• 3+ years: Data Architecture Guidelines Development.
• 3+ years: Security in Distributed Systems.
• 4+ years: Designing Scalable, Decoupled Systems.
• 5+ years: Strategy & Roadmap Creation.
• 3+ years: Influencing with Data-Driven Insights.
Domain Experience Required
• 4+ years: Functional Knowledge of Insurance Domains (Policy, Claims, Services Ops) - Preferred.
• 2+ years: Legal & Compliance Regulations in Insurance - Preferred.
• 3+ years: Data Product Development for Functional Domains.
• 2+ years: AI-Driven Business Process Automation