Andersen provides access to 90+ senior AI engineers experienced in generative AI, machine learning, data engineering, and intelligent systems, helping you add scarce AI talent and accelerate delivery.
Hire AI Developers
Our AI-native engineering practice integrates AI into every stage of software delivery, helping teams automate repetitive tasks, accelerate development, and improve code consistency. This approach reduces manual engineering effort by up to 35% and shortens delivery cycles by up to 30%, enabling faster releases while maintaining quality, security, and governance standards.
Work with expert AI developers
Our specialists have delivered 120+ AI projects across forecasting, insurance automation, medical imaging, computer vision, and conversational AI in production environments.
After aligning on your role, technology stack, and delivery goals, Andersen can onboard relevant specialists within 2–4 weeks, accelerating project execution and measurable progress.
AI developers we provide
Hire AI developers who combine applied AI knowledge with sound software engineering. We match the role to the business outcome, data maturity, and delivery environment, from a focused prototype to secure production rollout. Choose the specialist type that fits your delivery model, technology landscape, and operational goals rather than relying on a generic staffing list.
Forward-deployed AI engineers work beside business and operational teams to translate an unclear need into a tested solution. They combine discovery, AI development, and integration work to deliver AI-driven solutions that fit existing systems and improve outcomes through continuous user feedback.
Work typically results in:
- Use-case discovery and feasibility assessment
- Workflow-specific AI prototypes
- Integration with operational tools
- Feedback-driven production improvements
Machine learning engineers turn historical and live data into predictions, classifications, and recommendations. They select AI algorithms, prepare model training pipelines, and build AI models aligned with business metrics. Their work makes analytical systems operational, explainable, and maintainable in production environments.
Expected deliverables include:
- Supervised and unsupervised ML models
- Feature engineering and training pipelines
- Recommendation and classification systems
- Validation and performance reports
These specialists turn private knowledge and business content into useful, governed generative AI experiences. They design model, retrieval, and integration flows that let teams automate work without losing control of the output. Their expertise includes enterprise deployments built on Anthropic Claude, Amazon Bedrock, and modern RAG architectures.
Engagements can include:
- LLM-powered assistants and copilots
- Retrieval-augmented generation pipelines
- Prompt engineering and evaluation workflows
- Governed text, code, and content generation
AI agent developers design systems that plan tasks, call tools, and complete multi-step workflows with human oversight. The result is practical automation that can act across approved business applications instead of producing isolated answers. They also build MCP-enabled workflows and AI-assisted operations for enterprise environments.
Your workflow can gain:
- Tool use and function-calling agents
- Multi-agent orchestration workflows
- Memory and context retrieval layers
- Approval and escalation mechanisms
Data scientists examine raw data, identify meaningful variables, and create models that support better decisions. Their work connects business questions about demand, risk, or customer behavior with measurable data science outcomes and testable analytical results.
Analytical deliverables may cover:
- Exploratory data analysis
- Predictive models and statistical forecasts
- Experiment design and A/B analysis
- Decision dashboards and insights
AI data engineers create dependable data foundations for model development and AI integration. They organize collection, transformation, validation, secure data handling, and delivery processes so models receive trustworthy information at scale.
Data foundations can include:
- ETL and ELT data pipelines
- Data lakes, warehouses, and lakehouses
- Data quality and validation controls
- Secure feature and dataset management
MLOps engineers create the controlled infrastructure that keeps machine learning systems reliable in production environments. They automate releases and monitoring so teams can deploy AI models, detect drift, and respond before performance declines across major cloud computing platforms.
To maintain model reliability, we set up:
- Automated model deployment pipelines
- Experiment tracking and model registries
- Quality, latency, and drift monitoring
- Reproducible cloud-based ML environments
Computer vision engineers enable software to interpret images and video through deep learning and neural networks. They build reliable visual workflows for inspection, document processing, diagnostics, real-time assistance, and edge AI deployments where low-latency processing is required.
Visual capabilities can include:
- Object detection and image classification
- Segmentation and anomaly detection
- OCR and document image processing
- Real-time video analysis pipelines
NLP and conversational AI engineers build systems that understand, process, and generate natural language. Their expertise in natural language processing improves customer and employee interactions through searchable knowledge, chat, voice, and document automation.
Customer-facing capabilities include:
- Conversational AI assistants
- Text classification and entity extraction
- Speech-to-text and voice workflows
- Semantic search and document summarization
AI solution architects define how data, applications, APIs, models, and cloud services should operate as one scalable system. Their designs balance performance, security, cost, and maintainability, including cloud-agnostic architectures built around platforms such as Amazon Bedrock and modern AI ecosystems.
Architecture work produces:
- End-to-end AI solution architecture
- Model and infrastructure selection
- Integration and security blueprints
- Scalability and deployment strategies
Predictive analytics engineers make historical and real-time data useful for forecasting, risk detection, and planning. They turn models into accessible features, dashboards, and predictive analytics tools that business teams can act on.
Business teams receive:
- Demand and sales forecasting
- Risk and anomaly detection
- Scenario modeling and simulation
- Decision-support applications
AI QA and model evaluation engineers verify that AI features remain accurate, safe, and consistent before and after release. They create test data and edge cases that make quality measurable rather than assumed while supporting responsible and ethical AI deployment practices.
Quality assurance covers:
- Automated AI test suites
- Model quality and regression evaluations
- Hallucination, bias, and safety checks
- Production monitoring and evaluation reports
AI developers to hire ready to join your team
Hire AI engineers through staff augmentation and receive matching CVs in 2–4 weeks. Every profile below is a specialist available for interview now.
Ethan C.
AI Platform and Operation Engineer
summary
Specializes in designing, automating, and maintaining scalable and custom AI/ML platforms, ensuring reliable model deployment, monitoring, and lifecycle management across enterprise environments.
Maya L.
AI Agent Solution Engineer
summary
Builds autonomous and multi-step custom AI agents and orchestrated workflows that automate complex business processes using reasoning, tool-use, and dynamic decision-making.
Lucas V.
Conversational AI Solution Engineer
summary
Designs advanced conversational systems – including chatbots, voicebots, and RAG assistants – delivering natural, context-aware interactions powered by enterprise LLMs.
Selected projects delivered by our AI developers
Clients who hire AI developers from Andersen get production-ready solutions built around a defined business problem, an appropriate model or framework, and a measurable operational result. These projects span forecasting, insurance, diagnostics, hiring, automotive safety, and personalized services.
Austria
AI forecasting tool
For an Austrian AI company, Andersen built a forecasting MVP with React, RxJS, TypeScript, Python, Java, and R. In 15 months, it enabled two-plus years of data uploads, adjustable forecasts, and collaborative planning, helping the client win a state-financing tender and its first customers.
When to hire AI developers
Hire AI engineers when a measurable business constraint calls for more than conventional automation or reporting.
Repetitive manual work slows your team down
When staff spend hours copying data, reviewing documents, or routing requests, throughput is limited by manual handling. A specialist can automate classification, extraction, approvals, and AI-driven workflows, freeing capacity for higher-value work and making time saved visible.
Your data is collected but not used
Data spread across files, databases, and tools cannot guide decisions effectively. Specialists can create data pipelines, machine learning models, and predictive analytics tools that turn fragmented information into forecasts, risk signals, and recommendations.
Customer support cannot scale with demand
Growing demand can lengthen response times and make service inconsistent. A specialist can implement virtual assistants and conversational AI solutions connected to approved knowledge, resolving routine queries while escalating complex cases to people.
An AI prototype has to reach production
A promising prototype still needs secure data handling, integration, monitoring, and rollback controls. MLOps and delivery engineers work together to deploy AI solutions and deploy AI models through repeatable pipelines, evaluations, and observability.
Your in-house team lacks AI and ML expertise
Your product team may know the domain but not prompt engineering, computer vision, agentic AI, or production model operations. Hire AI experts to add the missing capability without delaying the initiative for a full internal hiring cycle.
You need to meet AI compliance requirements
Regulated use cases require traceability, controlled access, and evidence that a model behaves as intended. Our specialists design ethical AI safeguards, evaluation checkpoints, and audit-ready data handling that align AI with applicable rules and governance requirements.
AI technologies our team works with
Our AI-native engineering approach embeds intelligent automation across requirements, design, implementation, testing, and deployment—not as an ungoverned add-on. We choose the stack around model behavior, data volume, security needs, and cloud environment so AI-powered solutions fit existing systems and business goals.
Infrastructure and DevOps | MLOps platforms and model lifecycle |
|---|---|
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AI/ML frameworks | LLM and Agentic AI ecosystem |
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Back-end and API development | Speech, audio, and multimodal |
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Computer vision stack | |
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How our AI developers accelerate software delivery
AI developers at Andersen integrate AI throughout requirements management, architecture, development, testing, release management, and delivery operations. This approach reduces repetitive work, improves visibility, and helps teams deliver software faster while maintaining governance and quality standards.
Stage
Requirements
Architecture
Development
Testing
Release
ManagementWithout AI
3–4 weeks per epic
2–3 weeks (ADRs + diagrams)
100 hours per feature (baseline)
2 weeks per release
MTTR 4–6 hours per incident
3–4 hours/week on status reports per PM
With AI
1.5–2 weeks per epic
1–1.5 weeks
50–65 hours per feature
1–1.2 weeks per release
MTTR 2–3 hours
15–30 min/week
AI workflows
Requirement drafting, user story generation, gap detection
ADR generation, C4 diagram creation from code
Code generation, AI review, auto-docs
Test generation, regression scoring, self-healing
Risk scoring, pipeline automation, self-healing
Portfolio summaries, delay predictions, auto-status
Deliverables
User stories, ACs
ADRs, C4 diagrams
Code, unit tests, API updates
Test cases, scripts, reports
Safe deploy configs
Dashboards, briefings
* These figures and metrics represent market averages and may vary depending on the specific project or request.
Benefits of hiring AI developers
Faster time to market for AI features
A dedicated specialist avoids forcing the existing team to master every model, framework, and deployment pattern before the first release. Reusable components and validated AI tools shorten the route from use case to production-ready AI features.
Lower cost than building an in-house AI team
Building internally often requires several scarce roles: data scientists, architects, ML engineers, and MLOps specialists. Flexible staffing lets you pay for the skills required at each stage rather than carry recruiting, onboarding, and bench costs.
Access to scarce AI and ML skills
AI projects need different expertise at different moments, from generative AI and AI agents to data engineering or evaluation. A broader talent pool provides that combination when it is needed, rather than only when a local search succeeds.
Why hire AI developers from Andersen
Vetted AI engineers with production experience
Our AI engineers have delivered forecasting, insurance, medical imaging, hiring, automotive, and concierge products. Andersen is also expanding its Anthropic-certified delivery bench, with 13 specialists certified in June 2026 and a roadmap toward 500 certified experts by year-end and 2,000+ by 2028.
Senior-heavy teams with domain expertise
Companies that hire artificial intelligence engineers from Andersen get specialists combining engineering depth with experience across finance, healthcare, insurance, automotive, and e-commerce. This helps teams build AI-powered solutions aligned with business logic, market trends, regulations, and operational constraints.
Interviews and matching within days
When you hire AI engineers from Andersen, we clarify the role, profile specialists, confirm availability, and present CVs. Most engagements move from requirements to onboarding in 2–4 weeks, enabling interviews within days while maintaining a rigorous technical and domain-specific evaluation process.
Certified security and compliance
Our delivery practices are supported by ISO/IEC 27001, AICPA SOC 2, ISO 9001:2015, GDPR, and AWS machine-learning certifications. We also build enterprise solutions on technologies such as Amazon Bedrock and Claude, applying controlled access, audit trails, and human oversight in production environments.
Full ownership of IP and source code
Project-based and dedicated engagements can transfer the agreed source code, documentation, and intellectual property to your organization. Ownership terms are defined upfront, giving you full control over custom AI solutions tailored to your business, data, and operational goals.
Transparent pricing with no hiring risk
Choose a specialist, dedicated team, or project delivery model based on your goals. The agreed structure defines roles, deliverables, timelines, and team composition upfront, creating a predictable foundation for AI software development throughout the entire AI journey.
Certifications and partnerships
Andersen combines engineering delivery with formal security, quality, cloud, and project-management credentials. These certifications and partnerships provide an additional governance layer for organizations that need controlled data handling, documented processes, and production-ready AI solutions.
How much does it cost to hire a full time AI developer?
Steps to hire AI engineers from us
Our AI engineers reach your team through a process that turns a role request into a verified, delivery-ready match.
An Andersen expert defines your goals, role profile, technology stack, start date, and engagement model. Whether you plan to hire AI developer talent or build a larger team, you receive a requirements brief covering needed skills, seniority, responsibilities, and expected contribution to your AI software development initiative at its current stage of the AI journey.
Cooperation models
AI staff augmentation
Companies that hire AI developers through staff augmentation usually own product direction and architecture but need one specific capability. An Andersen engineer works through your tools, meetings, repositories, and quality standards. This model gives organizations access to specialized AI talent and skilled AI developers without changing existing delivery processes.
Dedicated AI development team
A dedicated AI development team supports a long roadmap, multiple workstreams, or continuous product development. Andersen provides engineering specialists, QA, architects, and other roles while you set priorities. This model is often chosen by organizations that need to hire dedicated AI developers and scale delivery across the AI journey.
Project-based AI delivery
Project-based AI delivery fits a defined MVP, feature, migration, or model integration with milestones and acceptance criteria. Andersen manages work from planning through QA and deployment, delivering a clear scope and outcome. This approach works well for AI software development initiatives that require custom AI solutions tailored to specific business objectives.
Meet our expert
Marcin Wawryszczuk
Head of AI Department
18
years of experience
100+
AI projects in his portfolio
10+
research papers
Marcin is an experienced AI architect with global leadership experience, holding both MBA and PhD degrees.
- Specializes in GenAI/ML Architecture;
- Expert in Agentic AI and RAG ecosystems building;
- Active Assistant Research Professor.
Industries our AI developers work in
We start from the business priority, not the model, and tie each AI initiative to a measurable operational result. This is how teams move from pilot ideas to production outcomes with clearer ROI, lower risk, and faster execution.
- Clinical decision-support assistants;
- Medical imaging analysis and diagnostics;
- Patient triage and care navigation;
- Automated medical-document processing.
- Fraud-detection and risk-scoring models;
- Customer onboarding automation;
- AI-driven financial forecasting;
- Document and compliance review assistants.
- Engineering copilots for requirements traceability and defect triage;
- Assistant workflows for knowledge transfer between distributed R&D teams;
- Multimodal interfaces for diagnostics and service knowledge support.
- Product recommendation engines;
- Demand forecasting and inventory optimization;
- Customer-service copilots;
- Dynamic pricing and promotion optimization.
- Editorial copilots for multi-channel publishing and localization;
- Audience support tools that personalize interactions in near real time;
- Production assistants for transcript cleanup and rights-aware repurposing.
- Route optimization and planning;
- ETA prediction models;
- Warehouse automation assistants;
- Demand and supply-chain forecasting.
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FAQ
Start with the business goal, required capability, data environment, integration scope, and expected deliverables. The right specialist has relevant production and domain experience and can work within your software delivery model. Many organizations prioritize top AI developers and skilled AI developers with proven experience in similar AI projects, industries, and production environments.
- Similar AI projects and domain knowledge;
- Relevant AI frameworks and cloud platforms;
- Data and model evaluation capability;
- Production deployment, security, and human oversight.
Reach out to Andersen's AI team
What happens next?
An expert contacts you after having analyzed your requirements;
If needed, we sign an NDA to ensure the highest privacy level;
We submit a comprehensive project proposal with estimates, timelines, CVs, etc.
Customers who trust us
