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Looking to hire AI engineers who can move your project from development to production? Get a dedicated engineering team that works within your existing product and technical environment to design, build, integrate, and deploy machine learning, NLP, computer vision, and generative AI systems. You retain full code and IP ownership while our engineers handle the technical delivery.
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"AI engineer" covers several distinct disciplines. Here's what each role actually does on a project, so you know exactly who you're hiring and what they're responsible for.
Builds and trains predictive, classification, and recommendation models for business use cases. Handles data preparation, feature engineering, model selection, evaluation, tuning, and optimization, then prepares models for integration into production applications and workflows.
Works with large language models, RAG, prompt engineering, and fine-tuning to create language-based applications. They connect models with business data, documents, APIs, and existing systems for search, summarization, extraction, automation, and conversational experiences.
Focuses on AI systems that interpret images and video, including object detection, classification, segmentation, OCR, and visual inspection. Their responsibilities span model training and evaluation through integration into monitoring, automation, quality control, and visual analytics applications.
Takes care of the infrastructure and workflows that move machine learning models into production. This includes deployment pipelines, model versioning, monitoring, automated retraining, performance tracking, and cloud infrastructure needed to maintain AI systems after launch.
Builds generative AI applications and autonomous agent workflows using LLMs, retrieval systems, tool integrations, and model customization. Handles agent orchestration, function calling, prompt workflows, and fine-tuning techniques such as LoRA and QLoRA for specialized use cases.
Provides the data foundation AI systems depend on, from ingestion and transformation to validation, integration, and storage. They create reliable data flows across multiple sources to support model training, retrieval systems, analytics, and production AI applications.
Tell us about your project, the expertise you need, and where your team needs support. We'll help you identify the right AI engineering capabilities and engagement model for the work ahead.
Talk to an AI EngineerEvery engineer goes through technical screening and project-level evaluation before joining an engagement. We match their experience across machine learning, NLP, computer vision, generative AI, and MLOps to the requirements of your project.
Your hired engineers work within structured sprints, code reviews, technical discussions, and demos. A project lead provides oversight while the engineers remain directly involved in the architecture, development, testing, and deployment work.
Your engagement starts under an NDA, with clear ownership terms from the beginning. You receive the source code and project deliverables, giving your team control over the systems your hired engineers build.
Work directly with the engineers responsible for your project instead of relying on layers of communication. You can discuss technical decisions, review progress, provide feedback, and work with the project lead throughout the engagement.
From model development and LLM integration to computer vision and automation, our AI engineers bring specialized expertise to the systems your business needs to build, integrate, and take into production.
AI engineers apply machine learning and AI capabilities to specific business workflows, from intelligent search and recommendations to classification and prediction. They work with your data, application architecture, and requirements to develop AI functionality that fits the product.
Engineers create conversational AI experiences that connect with your knowledge bases, business systems, and customer workflows. They handle retrieval, conversation logic, integrations, and evaluation to support use cases ranging from customer service to internal assistance.
Predictive, recommendation, classification, and risk-scoring use cases require models trained around the right data and business objectives. Our engineers handle data preparation, model development, evaluation, tuning, and integration into applications and operational workflows.
AI engineers work with NLP and LLM technologies for document processing, information extraction, summarization, semantic search, and other language-based workflows. They connect models with business data and systems while implementing retrieval, prompting, evaluation, and application logic.
Computer vision engineers work on image and video applications such as object detection, classification, OCR, segmentation, and visual inspection. They handle model development and evaluation before integrating computer vision capabilities into the cameras, applications, and workflows that need them.
Engineers create generative AI applications and agent workflows that can retrieve information, use tools, make decisions, and complete multi-step tasks. They handle orchestration, model integration, tool calling, retrieval, and application logic around your specific business requirements.
AI and automation engineers combine workflow automation with intelligent decision-making for repetitive business processes. They can connect AI models with existing applications and systems to automate tasks involving documents, data extraction, classification, routing, and other rule-based workflows.
When an existing model needs greater specialization, engineers can fine-tune it for specific domains, tasks, or datasets. They also handle deployment, monitoring, performance evaluation, and ongoing optimization so models can operate as part of production applications.
Their agile way of working, competence, and friendliness are impressive.December 7, 2024
Citrusbug Technolabs handles a data extraction project for an education company. They're also responsible for managing an AI generation project for the client.
Citrusbug Technolabs has laid out and followed a clear project outline and consistently met deadlines. The team communicates well and demonstrates a strong understanding of the client's vision and goals. Their agile approach, high level of competence, and personable approach stand out.
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Their communication and ability to capture exactly what I was asking for were impressive.November 14, 2024
Citrusbug Technolabs built an automated system for a veteran medical company. The system needed to collect and store user data, generate customized letters based on surveys, and use AI to summarize reviews.
Citrusbug Technolabs delivered a functional automated system that successfully organized the client's records and auto-generated statements. The team was quick to respond, addressed all roadblocks, and communicated effectively. Overall, their ability to understand the client's needs stood out.
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They were very calm and talented, and they did bring some of their own ideas.January 28, 2025
Citrusbug Technolabs has developed an MVP for a freight and capacity marketplace. The team has created three different interfaces for three market segments.
Citrusbug Technolabs has successfully developed an effective MVP. The team has delivered the project on time and almost within budget. Moreover, their responses have been timely. Overall, the team has been calm, talented, and innovative. This has led to the client's satisfaction.
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What stood out most about them was their personalized approach and sense of ownership in every aspect of the project."February 27, 2024
Citrusbug Technolabs built a Flutter cross-platform mobile app for an education company. They also developed two web apps and an admin panel and provided DevOps support for the client's infrastructure.
Citrusbug Technolabs' support successfully boosted the client's user engagement by 8x, resulting in an 85% retention rate. The reliable team demonstrated a dedication to delivering on time and promptly responding to concerns and adjustments. Their personalized, client-centric approach stood out.
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They didn't like the phrase "impossible to do", which made them very solution-oriented.February 19, 2024
A healthcare company hired Citrusbug Technolabs to design and develop a user-friendly application. The team provided tailored assistance to meet the client's requirements.
Citrusbug Technolabs successfully delivered an application that was ready for market. The team was highly professional and well-organized; they stayed focused throughout the engagement and were receptive to changes. Overall, they stood out for their trustworthiness and commitment to excellence.
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They're 100% on time and transparent throughout each stage of the project.February 24, 2024
A financial technology company has hired Citrusbug Technolabs to build a custom web application. The team handles the development and design of the app.
The project is ongoing, but the client has been satisfied with Citrusbug Technolabs' services. The team is very professional — they always respect timelines and provide regular updates to ensure everyone's on the same page. Also, their transparent approach makes them a trustworthy partner.
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"I do believe they are exceptional at where they stand."
Citrusbug Technolabs took on a mobile app development project for a Singapore-based development house. They were provided with UX designs to execute. Currently, they're finalizing and fine-tuning the product.
Citrusbug Technolabs contributed to the successful launch of the product, which has garnered over 10,000 users in just three months. The product has also received support from investors. They consistently followed-up with their progress and delivered at a speedy rate. The team is truly stellar.
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We review experience across machine learning, NLP, computer vision, generative AI, and related technologies, looking at the engineer's hands-on work, technical depth, and experience taking AI systems into development and production.
Engineers walk through a realistic AI system design and explain their approach to architecture, model selection, data flow, integrations, scalability, and potential failure points. This helps assess how they approach real engineering decisions.
We consider the requirements of your project and match them against the engineer's technical background and industry experience. We also assess how clearly they communicate technical decisions with both engineering teams and business stakeholders.
Where appropriate, engineers complete a practical task based on the type of work they would handle on your project. A senior engineer reviews the work to assess implementation quality, reasoning, and attention to engineering details.
Once the evaluation is complete, we introduce engineers whose skills and experience align with your requirements. You can discuss the project directly with them before deciding how you want to structure the engagement.
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A full team, run by a Citrusbug lead
Citrusbug owns the outcome, not just headcount
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AI engineer rates typically range from $40 to $80 per hour, depending on seniority, technical expertise, project complexity, and engagement model. You can hire AI engineers through Fixed-Price, Time and Material, or Dedicated Team models, based on the scope and level of involvement your project requires. Share your details to get a scoped estimate for your project.
AI engineering has shifted from training models from scratch to applying pretrained LLMs in production, which means the tooling our engineers use looks different than it did two years ago. Our teams work in current frameworks, not legacy pipelines that stopped getting updates.
Once you decide to hire AI engineers through Citrusbug, we move from team selection into a structured delivery process. You get the right technical expertise, project context, and communication channels in place before engineering work begins.
We review your project requirements, technical stack, and required expertise to identify AI engineers who fit the work. You can discuss their experience and technical background before the engagement begins.
Your engineers get access to the necessary systems, documentation, data, and project context. We align on priorities, responsibilities, communication, and the first sprint so the team can start with a clear direction.
Engineering work runs through structured sprints with code reviews, technical discussions, demos, and regular progress updates. Your team stays involved in key decisions while the engineers handle the assigned development work.
Support continues beyond the initial development phase when your engagement requires it. Engineers can help with monitoring, troubleshooting, optimization, maintenance, and further improvements as your AI system evolves.
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Both. Staff augmentation adds individual engineers to your team; a dedicated team gives you a full unit with a project lead included.
We handle rematching directly. You're not managing a recruiting process again; we resolve fit issues as part of the engagement.
Yes. Full source code and trained model ownership transfers to you at delivery, with no ongoing licensing back to Citrusbug.
Yes. Engineers integrate with your existing cloud environment, CI/CD, and data infrastructure rather than requiring a separate stack.
A project lead, a matched engineering team, fixed sprint cadence, and end-to-end accountability for delivery, not just staffed headcount.
Yes. Post-launch monitoring, retraining, and fixes are included as part of the engagement, not sold separately afterward.
