Our AI staff augmentation services give you access to vetted AI engineers with the technical experience your project requires. They work within your existing development team, codebase, technology stack, development processes, and engineering environment, while your technical leadership continues to direct the work.
Tell us what you’re building, which skills you need, and when you need them. We’ll match you with qualified AI engineers based on your technology project requirements and timeline.
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AI staff augmentation gives engineering teams a practical way to add specialized technical expertise and additional development capacity without waiting through a full-time hiring process.AI projects often require skills that aren’t available inside the team you have today. You might need engineers experienced in production RAG, LLM integration, AI agents, model deployment, or the infrastructure required to run these systems reliably.Staff augmentation gives you access to additional engineering resources when your internal team needs more capacity, specialized skill sets, or experience with a particular area of AI development like AI solutions for eCommerce.
Our ai augmented engineering approach adds specialized engineers to your existing engineering organization rather than creating a separate delivery function around your product. The engineers work as part of your engineering team structure, following the same development practices and contributing to the same software development lifecycle as your internal engineers.
Engineers work directly within your repositories, architecture, development environment, and technology stack. They contribute to the systems your team already maintains instead of building a separate solution outside your product.
They use the tools and processes your team already relies on—from project management and version control to code reviews, sprint planning, documentation, and team communication.
Your CTO, VP of Engineering, engineering manager, or designated technical lead continues to set priorities, review technical decisions, and determine how the work fits into your broader product roadmap and business and technical objectives.
Your team decides what gets built, which problems take priority, and how the solution should evolve. The added engineers provide technical capacity without taking ownership of your product strategy.
Different projects require different technical profiles. Whether you need to hire ai engineer for an application team or bring in several specialists for a larger initiative, you can build the team around your project-based requirements.
Build and integrate AI capabilities into software products, internal platforms, and business workflows. These engineers can work across application logic, model integrations, APIs, data flows, and the surrounding software required to put AI features into production.
If you need to hire ai ml developers, you can add engineers who develop, train, optimize, and deploy machine learning models across the full model lifecycle.
If your project requires specialists who can hire generative ai engineers to build LLM-powered applications, these engineers can work across model integration, application architecture, evaluation, and the engineering required for production generative AI systems.
Work with engineers experienced in LLM integration, RAG, fine-tuning, evaluation, inference, and the application architecture surrounding large language models.
An AI agent engineer can develop AI agents that use tools, interact with external systems, and execute multi-step tasks. Their work can include agent architecture, orchestration, tool calling, memory, evaluation, and workflow design.
Build and maintain the infrastructure required to run machine learning and AI systems in production, including deployment, monitoring, model lifecycle management, cloud infrastructure, and operational reliability.
Build the data pipelines, processing systems, and infrastructure that AI applications depend on, supporting model training, retrieval systems, analytics, and production AI workflows.
Develop AI systems that work with images, video, and other visual data, including image classification, object detection, visual recognition, video analysis, and computer vision pipelines.
From AI augmented software engineering to production AI infrastructure, your team can add the technical capabilities required at different stages of development.
Build applications around LLMs and other generative models, including RAG systems, fine-tuning workflows, prompt engineering, model evaluation, and production AI applications.
Develop and deploy models for prediction, classification, recommendation, forecasting, and other machine learning use cases, from model training through production deployment.
Build systems in which AI agents can reason through tasks, call tools, interact with external systems, and coordinate multiple steps or specialized agents.
Build the infrastructure required to run AI systems reliably at scale, including deployment pipelines, cloud environments, monitoring, data pipelines, and model operations.
The right ai augmented development model gives your team access to specialized engineering capability while fitting into the way your organization already operates.
Our ai augmented development services are structured around your project requirements, technical stack, team structure, and timeline.
Share the project you’re working on, the technical challenges involved, the skills you need, preferred seniority, technology stack, team structure, and expected timeline. We use those details to understand your technical resource requirements and identify relevant engineers.
We identify engineers whose technical background and experience align with the role. You review their profiles and meet the candidates who fit your requirements and project delivery requirements.
Your team evaluates the candidates through your own interview and technical assessment process. You choose the engineers you want to work with based on their experience, technical capabilities, and fit with your team.
Once the engagement begins, the engineers join your development environment and working processes. They collaborate with your team and contribute to the project under your technical direction.
Build AI-powered applications around large language models and other generative AI technologies. Engineers can work on the application layer, model integration, retrieval, evaluation, and supporting production infrastructure.
Develop agents that can use tools, interact with APIs and other systems, make decisions, and execute multi-step workflows. This can include agent architecture, orchestration, tool calling, evaluation, and production integration.
Build retrieval-augmented generation systems that connect language models with proprietary documents and business data. Engineers can work across ingestion, chunking, embeddings, vector search, retrieval, generation, and evaluation.
Develop machine learning systems for prediction, classification, recommendation, forecasting, and other use cases. Engineers can contribute across data preparation, model development, training, evaluation, deployment, and monitoring.
Add AI capabilities directly to an existing SaaS or software product. This can include intelligent search, recommendations, document processing, conversational interfaces, content generation, classification, and other AI-powered functionality.
Build the infrastructure needed to deploy and operate AI systems in production, including model serving, cloud infrastructure, deployment pipelines, monitoring, data pipelines, observability, and model operations.
Choosing between AI augmented development and other hiring models depends on where the engineers sit, who directs the work, and how the engagement is structured.
When you hire artificial intelligence engineers, the technical fit matters as much as availability. The goal is to find engineers whose background matches the actual work your team needs done.
Access engineers with experience relevant to your project, whether you need an LLM specialist, ML engineer, MLOps engineer, AI agent developer, or another specialized role.
Candidates are evaluated for the technical capabilities relevant to their role before they are presented for consideration. Your team can then apply its own interview and assessment process before making a selection.
Work with engineers focused on artificial intelligence, machine learning, data, and related engineering disciplines rather than relying on general-purpose software staffing.
Review relevant candidates without spending weeks sourcing profiles across multiple recruiting channels. We handle the initial matching process around your technical requirements and technical talent pool.
Add one engineer for a specific capability, build a larger team for an AI initiative, or maintain additional engineering capacity for an ongoing project.
Engineers work within your codebase, technology stack, development tools, communication channels, and engineering processes.
Use appropriate confidentiality agreements and NDAs to establish obligations around proprietary information, business information, source code, and other confidential materials.
Define ownership of code, deliverables, inventions, and other intellectual property clearly in the engagement terms.
Give engineers access to the systems and resources required to perform their work while limiting access that is not required for the project.
Use your existing repository and permission controls to determine which codebases, branches, environments, and development resources each engineer can access.
Define how project data, credentials, customer information, and other sensitive materials may be accessed, stored, transferred, and handled during the engagement.
When an engagement ends, remove the engineer’s access to repositories, credentials, environments, communication tools, and other project systems according to your offboarding process.
Document confidentiality, intellectual property ownership, access requirements, data handling, and other relevant obligations in the engagement agreement.
The cost of ai staff augmentation services depends on the role, level of expertise, technical specialization, location, engagement length, and number of engineers required.
What affects the rate?
AI Engineer, ML Engineer, LLM Engineer, MLOps Engineer, and other specialized roles can have different rates based on their responsibilities and technical requirements.
More experienced engineers generally command higher rates, particularly when a project requires independent technical ownership or experience with complex production systems.
Specialized work such as LLM infrastructure, advanced machine learning, AI agents, or production MLOps can require deeper expertise than general AI application development.
Engineer location and required time-zone overlap can affect the available talent pool and engagement rate, including when teams consider offshore ai developers.
A short-term project and an ongoing engineering engagement can have different commercial structures.
Pricing changes depending on whether you need one specialist, several engineers with complementary skills, or a larger dedicated team.
Tell us what you’re building, which skills you need, how many engineers you’re looking for, and your expected timeline. We’ll recommend an engagement based on those requirements.
Whether you need to hire agentic developers, add an ai agent developer, or bring in an ai application developer to build and integrate AI-powered software, we can help you assemble the right technical team for the work.
For teams evaluating the best staff augmentation providers for ai and data engineering, the right fit comes down to the engineers, technical expertise, engagement model, and ability to work effectively within your existing organization.
Tell us what you’re building, which skills you need, and when you need them.
