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Artificial Intelligence Ai Engineer Jobs (NOW HIRING)

STI

Artificial Intelligence ("AI") Architect

Denver, CO · On-site +1

... artificial intelligence across government agencies. This role requires a unique blend of technical ... Experience in working with cross-functional teams, including engineers, data scientists, and ...

SCATR Corp.

Artificial Intelligence Engineer

Cleveland, OH · On-site

$111K - $133K/yr

We are looking to add an experienced Artificial Intelligence (AI) Engineer to our dynamic team and ... As an AI Engineer, you will be responsible for designing, implementing, testing and maintaining ...

Varsity Tutors

Artificial Intelligence (AI) Tutor

FL · Remote

$18 - $40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Artificial Intelligence (AI) tutors nationally. As a tutor on the Varsity Tutors Platform, you'll ...

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All JobsArtificial Intelligence Ai Engineer Jobs

Artificial Intelligence Ai Engineer information

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$39K

$101.8K

$137.5K

How much do artificial intelligence ai engineer jobs pay per year?

As of Aug 6, 2026, the average yearly pay for artificial intelligence ai engineer in the United States is $101,752.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,000.00 and $116,500.00 per year, depending on experience, location, and employer.

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

AI Engineers often encounter challenges when moving models from development to production, such as data drift, scalability issues, and model monitoring. Ensuring that models perform well on real-world data, keeping them up-to-date with changing inputs, and integrating them seamlessly with existing systems are key hurdles. Collaborating closely with data engineers, software developers, and product teams is crucial to address these challenges and maintain robust, reliable AI solutions.

What is the difference between Artificial Intelligence Ai Engineer vs Data Scientist?

AspectArtificial Intelligence Ai EngineerData Scientist
Required CredentialsBachelor's or master's in CS, AI, or related fields; programming skills in Python, Java; knowledge of ML frameworksBachelor's or master's in CS, statistics, or related fields; strong programming skills; expertise in statistical analysis
Work EnvironmentDevelops AI models, algorithms, and systems; collaborates with software teamsAnalyzes data, builds predictive models, interprets data insights
Employer & Industry UsageTech companies, AI startups, R&D departmentsFinance, healthcare, marketing, tech firms

While both roles require programming and analytical skills, Artificial Intelligence Ai Engineers focus on designing and implementing AI systems and algorithms, whereas Data Scientists analyze data to extract insights and build predictive models. AI Engineers are more involved in developing AI solutions, while Data Scientists interpret data to inform business decisions.

What are the key skills and qualifications needed to thrive as an artificial intelligence AI engineer?

To thrive as an Artificial Intelligence (AI) Engineer, you need strong programming skills (especially in Python or Java), a deep understanding of machine learning algorithms, and a background in computer science or related fields, often with at least a bachelor's degree. Experience with frameworks like TensorFlow, PyTorch, and knowledge of cloud platforms or relevant certifications are highly valuable. Critical thinking, problem-solving abilities, and effective communication help AI Engineers collaborate and innovate in multidisciplinary teams. These skills are crucial for developing, deploying, and optimizing AI solutions that address complex business and technical challenges.

What is an artificial intelligence AI engineer?

Artificial Intelligence (AI) Engineers are professionals who design, develop, and implement AI models and systems to solve complex problems. They use techniques from machine learning, deep learning, and data science to create intelligent applications that can analyze data, recognize patterns, and make decisions. AI Engineers work across various industries, including healthcare, finance, and technology, to build solutions such as chatbots, recommendation systems, and autonomous vehicles. Their role often involves programming, data analysis, and collaborating with other teams to ensure AI systems are effective and ethical.
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Artificial Intelligence Ai Engineer jobs near you
Infographic showing various Artificial Intelligence Ai Engineer job openings in the United States as of July 2026, with employment types broken down into 73% Full Time, 24% Part Time, and 3% Contract. Highlights an 65% Physical, 3% Hybrid, and 32% Remote job distribution, with an average salary of $101,752 per year, or $48.9 per hour.

Artificial Intelligence Engineer (AI/ML)

Hireblazer

Austin, TX • On-site

$113K - $136K/yr

Contractor

Re-posted 17 days ago

Job description

Role: Artificial Intelligence Engineer (AI/ML)

Duration: 12 Months

Location: Austin, TX 78744

Note: This role requires onsite presence 4–5 days per week.

The client work to be accomplished:

The Artificial Intelligence (AI) engineer will develop AI/ML proof-of-concept demonstrations and build new AI/ML solutions that scale with the client pipelines and workflows. The role will be embedded within the Traffic Technology team with the goal to develop AI/ML for Operational Technology that improves the safety and operations of the client roadway system. The AI engineer will work across teams such as Traffic Technology, ITD AI team, and TRF to gather business requirements, develop AI software, and demonstrate successful solutions to end-users.

Core Responsibilities

Gather and document AI solution requirements from business stakeholders.

Develop AI proof-of-concepts and transition successful prototypes into production systems.

Design and implement scalable AI pipelines for enterprise applications.

Train, fine-tune, and validate AI/ML models for optimal performance.

Write clean, efficient software code and scripts for AI workflows.

Conduct rigorous testing and quality assurance of AI models and outputs.

Ensure compliance with organizational IT governance, security, and audit standards.

Stakeholder Engagement & Communication

Act as liason between Traffic Technology team, business stakeholders, and automation developers.

Facilitate requirements gathering and ensure clarity in AI solution design.

Communicate progress, risks, and issues to project sponsors and leadership teams.

Delivery Excellence & Governance

Ensure automation projects comply with the client’s IT governance, security, and audit requirements.

Promote reusable components and standardized AI development practices.

Conduct post-implementation reviews to capture lessons learned and improve delivery methods.

Team Coordination & Support

Collaborate with data engineers, business analysts, and infrastructure teams.

Provide guidance on AI best practices and assist in troubleshooting.

Support knowledge sharing and continuous improvement within the team.

Required Skills:

Python – 1-3+ years production experience, this is your primary language

AI/ML Production - Built and deployed 1-3+ ML models serving real users, not just experiments

Cloud Platforms - Experience with AWS, Azure, GCP, or OCI for deploying and managing ML workloads. We leverage AI/ML tools across all major cloud providers (Azure AI, AWS SageMaker/Bedrock, GCP Vertex AI, OCI AI Services)

DevOps - Docker and Kubernetes experience

Databases - SQL (PostgreSQL, MySQL) and NoSQL/vector databases

Scripting - Proficient in both Bash and PowerShell for automation

Command Line Interface (CLI) – 1-3+ years production experience working in CLI terminal.

Preferred Skills and Qualifications

CI/CD Experience: Azure DevOps, GitHub Actions, Jenkins, or similar automation pipelines

Computer Vision: Production CV experience with PyTorch/TensorFlow, OpenCV, object detection, segmentation, or real-time inference

Additional Languages: Go or Rust experience for performance-critical components

Feature stores (Feast, Tecton) or advanced feature engineering

Model optimization: quantization, pruning, knowledge distillation

Edge deployment or resource-constrained model deployment

Experiment frameworks for A/B testing ML models

Contributions to open-source ML projects

Real-time streaming data processing (Kafka, Kinesis)