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Backend Ai Engineer Jobs (NOW HIRING)

BrightEdge

Senior Backend AI Engineer

Cleveland, OH · On-site

Sr. Backend AI Engineer Location:  Cleveland, OH Role Overview Move beyond traditional ETL. At BrightEdge, we are rebuilding our core data infrastructure into an  AI-augmented processing engine

BrightEdge

Senior Backend AI Engineer

Cleveland, OH · On-site

Sr. Backend AI Engineer Location: Cleveland, OH Role Overview Move beyond traditional ETL. At BrightEdge, we are rebuilding our core data infrastructure into an AI-augmented processing engine . We're ...

BrightEdge

Senior Backend AI Engineer

Cleveland, OH · On-site

Sr. Backend AI Engineer Location: Cleveland, OH Role Overview Move beyond traditional ETL. At BrightEdge, we are rebuilding our core data infrastructure into an AI-augmented processing engine . We're ...

Saviance

Senior Backend AI Engineer

Saviance is a company seeking a Senior Backend AI Engineer with expertise in Python and medical provider credentialing. The role involves developing backend services and AI-assisted workflows to ...

Traversal

AI Engineer - Backend

Manhattan, NY · On-site

As a Backend AI Engineer, you will design and build core systems for real-time incident detection and automated remediation, collaborating with AI and infrastructure teams to optimize performance and ...

Reducto

Backend/AI Engineer

San Francisco, CA · On-site

$200K - $300K/yr

The Opportunity As a Backend/AI Engineer, you'll work on our core API that powers document parsing for hundreds of companies. You'll integrate cutting-edge LLMs, optimize document processing ...

Apex Informatics

Java AI Developer - Backend AI

Phoenix, AZ · On-site

$50.25 - $65/hr

Java AI Developer - Backend AI Location: Phoenix, AZ/ Charlotte, NC Key Responsibilities * Develop and maintain backend services using Java and Spring Boot * Build event-driven systems using Kafka ...

Traversal

AI Engineer - Backend

New York, NY · On-site

$150K - $300K/yr

The Role As a Backend AI Engineer at Traversal, you'll play a key role in designing and building the core systems behind our AI site reliability engineer, the infrastructure that enables real-time ...

Emanate

AI Engineer

San Francisco, CA · On-site

As a Backend/AI Engineer at Emanate, you'll work on the core infrastructure that powers our AI revenue engine for companies that are building the backbone of the physical economy. You'll integrate ...

Open Select

Member of Engineering, Backend - AI

$290K/yr

Member of Engineering, Backend - AI Location: Fully remote (East Coast preferred) Salary: Up to $290k + equity Industry: AI, Developer Platform, Software Engineering Role Overview Help power the ...

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Backend Ai Engineer information

See salary details

$60.5K

$147.7K

$199K

How much do backend ai engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for backend ai engineer in the United States is $147,662.00, according to ZipRecruiter salary data. Most workers in this role earn between $124,000.00 and $172,000.00 per year, depending on experience, location, and employer.

What is a Backend AI Engineer?

A Backend AI Engineer is a software engineer who specializes in building and maintaining the server-side infrastructure for artificial intelligence applications. Their work involves designing APIs, integrating machine learning models, managing databases, and ensuring efficient data flow between systems. They collaborate with data scientists and frontend developers to deploy AI models at scale and make them accessible through robust backend services. Key skills for this role include programming (often in Python, Java, or similar languages), cloud computing, and knowledge of AI frameworks.

What are the key skills and qualifications needed to thrive as a backend AI engineer?

To thrive as a Backend AI Engineer, you need strong programming skills (especially in Python or Java), a deep understanding of algorithms and data structures, and a background in computer science or related fields. Familiarity with AI/ML frameworks (like TensorFlow or PyTorch), RESTful APIs, databases, and cloud platforms is typically expected, along with relevant certifications. Exceptional problem-solving abilities, teamwork, and effective communication are soft skills that distinguish top performers. These competencies are crucial for designing robust, scalable AI solutions that integrate seamlessly with backend systems and drive innovation.

What are some common challenges backend AI engineers face when deploying machine learning models to production?

Backend AI Engineers often encounter challenges such as ensuring model scalability, maintaining low latency, and handling diverse data inputs during deployment. Integrating models into existing backend systems can also require careful consideration of APIs, security, and resource management. Additionally, monitoring model performance and updating models with new data are ongoing responsibilities that require close collaboration with data scientists, DevOps, and product teams.

What is the difference between Backend Ai Engineer vs Data Scientist?

AspectBackend Ai EngineerData Scientist
Required CredentialsBachelor's in CS, AI, or related field; knowledge of programming, AI frameworksBachelor's or higher in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops AI models, integrates AI into backend systems, collaborates with software teamsAnalyzes data, builds models, interprets data insights, collaborates with business teams
Industry UsageTech companies, AI startups, software firmsResearch institutions, tech companies, finance, healthcare
Common Search/ComparisonYesYes

While both roles involve working with AI and data, Backend Ai Engineers focus on integrating AI models into backend systems and developing scalable AI solutions. Data Scientists primarily analyze data, build predictive models, and generate insights. The roles often overlap in skills and tools but differ in their core focus—system integration versus data analysis.

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Backend Ai Engineer jobs near you
Infographic showing various Backend Ai Engineer job openings in the United States as of August 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution, with an average salary of $147,662 per year, or $71 per hour.

Senior Backend AI Engineer

BrightEdge

Cleveland, OH • On-site

Full-time

Re-posted 12 days ago

Job description

Sr. Backend AI Engineer 
 
Location: Cleveland, OH  
 
Role Overview Move beyond traditional ETL. At BrightEdge, we are rebuilding our core data infrastructure into an AI-augmented processing engine. We’re looking for a Senior Backend Engineer to architect "intelligent" systems that don't just move data, but understand it. You will build self-healing pipelines and agentic workflows that allow our platform to autonomously adapt to complex, shifting data landscapes.  
 
What You’ll Do 
 
Architect Agentic Pipelines: Build Python-based backend systems that utilize LLMs and AI agents to automate the discovery, parsing, and mapping of unstructured data. 
Build Self-Healing Infrastructure: Develop autonomous reliability layers that diagnose upstream failures, adapt to API schema changes, and perform real-time data validation without manual intervention. 
Optimize High-Velocity Analytics: Design and manage low-latency data flows into ClickHouse and BigQuery, ensuring our AI-driven insights are backed by high-performance OLAP architecture. 
Scale Intelligent Connectors: Create robust integrations (APIs, Streaming, S3) that leverage AI to handle diverse, non-standardized data sources at massive scale. 
Engineering Leadership: Drive best practices in AI-assisted testing and observability, mentoring the team on the intersection of traditional backend stability and modern AI capabilities.  
 
What You Bring 
 
5+ years experience with Bachelor's degree in Computer science, Software Engineering
Python & AI Mastery: Deep expertise in production-level Python. You’ve successfully integrated AI models/LLMs into backend services or data workflows. 
OLAP Power User: Proven experience with ClickHouse, BigQuery, or Snowflake, specifically in schema optimization and materialized views for large-scale datasets. 
Distributed Systems Expertise: Strong background in cloud environments (GCP/AWS) and real-time streaming tools like Kafka, Flink, or Spark. 
Adaptive Mindset: You don't just build to a spec; you build systems that anticipate and recover from environment changes autonomously. 
Product Ownership:A track record of leading high-impact initiatives and a desire to own the "brain" of a global data platform