AI Engineer - Remote

Deepixel; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

Fully remote

Mentoring and professional development

Job summary

Deepixel is hiring a role focused on developing practical AI features for business use. You will work with software, data, cloud, quality and consulting teams to integrate language models, build agent workflows and retrieval systems, and prototype AI use cases across cloud platforms.

The role emphasizes experimenting with new AI approaches while ensuring secure, reliable, scalable solutions that are useful in real settings. Fully remote with international client exposure.

Qualifications

• Degree or equivalent practical experience in CS/AI/DS/software engineering.
• Software development fundamentals and API/data integration experience.
• Strong analytical and problem-solving abilities; ability to assess AI output quality.
• Clear written and verbal communication; able to work independently and with distributed teams.
• Interest in researching and learning fast-changing AI technologies.

Responsibilities

• Integrate language models and generative AI services into applications and backend systems.
• Build agent workflows, automation, retrieval-augmented generation pipelines, semantic search and document processing.
• Work with embeddings, vector databases, structured and unstructured data, and model orchestration.
• Prototype use cases and compare models, APIs and implementation options.
• Add safeguards, validation and error handling; assess output quality, consistency and reliability.
• Deploy, support and tune cloud-based AI applications, including latency and operating costs.
• Coordinate with development and QA teams, document implementations and contribute to technical direction.

Skills

Analytical thinking

Problem solving

Communication

Independent work

AI technologies

Education

CS/AI/DS/Software Eng degree or equivalent

Tools

Docker

Git-based development workflows

AWS

Azure

Job description

Role overview

Develop practical AI features and applications for business use, working with software, data, cloud, quality and consulting colleagues. The work spans generative AI and language model integrations, agent workflows, retrieval systems and document-focused applications. Engineers are expected to experiment with emerging approaches while keeping solutions secure, reliable, scalable and useful in real settings.

Responsibilities

• Integrate language models and generative AI services into applications and backend systems.
• Build agent workflows, automation, retrieval-augmented generation pipelines, semantic search and document processing.
• Work with embeddings, vector databases, structured and unstructured data, and model orchestration.
• Prototype use cases and compare models, APIs and implementation options.
• Add safeguards, validation and error handling; assess output quality, consistency and reliability.
• Deploy, support and tune cloud-based AI applications, including their latency and operating cost.
• Coordinate with development and QA teams, document implementations and contribute to technical direction.

Requirements

• Degree in computer science, AI, data science, software engineering or a related field, or equivalent practical experience.
• Software development fundamentals and familiarity with integrating applications, APIs and data.
• Strong analytical and problem-solving skills, including the ability to assess AI output quality.
• Clear written and verbal communication, and the ability to work independently as well as with a distributed team.
• Interest in researching and learning fast-changing AI technologies.

Nice to have

• Exposure to Docker, Git-based development workflows, AWS or Azure.
• Experience with data preparation, processing or evaluation.

Benefits and work setup

• Fully remote work with exposure to real-world, including enterprise and international client, projects.
• Hands-on work with language models, agents, retrieval systems, multiple APIs and cloud platforms, plus mentoring and professional development.
• Opportunities to take on more technical ownership over time and progress toward senior engineering, technical leadership, consulting, machine learning or AI architecture roles.