AI Engineersfor your AI projects
Delivery from nearshore and offshore
The prototype works, but the path to production is missing: data integration, integration with existing systems, evaluation and stable operations. There is rarely enough internal time for this, especially with several AI initiatives running alongside day-to-day business.
Delvera provides vetted mid-level and senior AI engineers with experience in the platforms you use. Ready to start in 14 days, coordinated from Munich.
For companies and IT service providers with ongoing AI projects, specific skill gaps or critical deadlines.
- 14 days
- to project start after your approval
- 72 hrs
- to 3-5 concrete proposals
- 5 areas
- Products from Azure OpenAI to MLOps
- Mid + Senior
- no junior profiles
When external AI engineers make sense
For long-term core roles, your own recruiting remains the right path. External AI engineers help when an AI project has to start or continue before an internal hire is realistic.
From prototype to production
The pilot works in the demo, but monitoring, scaling and error handling for operations are missing.
LLM and agent integration
An existing system is to be extended with RAG, chat features or agent workflows.
Data foundation for ML and AI
Training and evaluation data has to be prepared, cleaned and versioned before a model works reliably.
Model operations and MLOps
A model is to run in production with versioning, monitoring and retraining. Internal MLOps know-how is missing or fully booked.
Platforms and products
Select an area. Further platforms by agreement, and for specialised needs we search specifically.
Azure
Azure OpenAI and Azure AI Foundry
LLM applications in the Microsoft ecosystem: deploy and secure models and integrate them into existing systems.
- Azure OpenAI Service
- Azure AI Foundry
- Azure AI Search for RAG
- Microsoft Copilot Studio
Nearshore or offshore?
Both can be combined in one setup. We agree on the right model before the project starts.
| Criterion | Nearshore | Offshore |
|---|---|---|
| Best for | Close alignment, architecture work and collaboration with internal data science teams | Clearly specified delivery, data preparation and pipeline work |
| Typical tasks | RAG architecture, agent workflows, integration into core systems | Evaluation pipelines, fine-tuning to specification, MLOps automation |
| Working hours | Compatible with German business hours | Compatible with German business hours |
| Typical regions | Poland, Romania, Hungary, Bulgaria, Portugal, Croatia, Ukraine | Vietnam, India, Indonesia |
| Contract partner | Delvera GmbH, Munich | Delvera GmbH, Munich |
| Start | Within 14 days of your approval | Within 14 days of your approval |
Partner and profile check
In AI engineering, a well-written CV is not enough. What matters is whether the experience fits the specific use case.
We distinguish between tool experience and relevant project experience. Profiles are matched against your specific context: target picture, tech stack, project phase, communication needs, seniority and availability.
- Comparable project experience with production AI systems
- Technology depth in the relevant stack
- Seniority in the specific project context
- Experience with DACH clients, mid-market and enterprise
- Communication quality and reliability
- Availability and a realistic start plan
Typical engagements
Typical starting points, not specific client references.
A pilot needs to go live
Starting point
An AI pilot convinced internally, but monitoring, scaling and error handling are missing.
Setup
AI engineer with MLOps experience, deployed for the project.
RAG system for internal knowledge
Starting point
Internal documents are to be made searchable through a RAG system, and integration capacity is missing.
Setup
AI engineer with vector database experience and Azure OpenAI or Bedrock.
Data foundation for an ML project
Starting point
An ML initiative needs clean, versioned training data before it can start.
Setup
AI engineer with data pipeline experience, complemented by a data engineer.
How we work together.
Every request starts with a concrete requirement intake. We then validate matching specialists from our network and coordinate the project start in under 14 days.
- 01
Define requirement
We clarify requirement, duration, budget range and project start. No pitch, no deck.
Day 0 - 02
Service and project setup
We design the right setup from our international engineering network.
1-3 days - 03
Partner and profile check
We check partners and profiles: experience background, communication and fit to the project context. Only then do we introduce specialists to you.
internal - 04
Coordinate intro calls
3-5 concrete proposals within 72 hours with documented background. We coordinate the calls with the specialists, you decide.
72 hrs - 05
Coordinated project start
After your approval: project start in under 14 days, coordinated by Delvera. Delvera remains your central point of contact.
< 14 days
Who it is for
CTOs and IT leaders
When AI projects have to deliver but internal experience with the platforms in use is missing.
Product and AI owners
When an AI feature or an internal LLM-based tool quickly needs additional hands.
IT service providers
When a client AI project starts or grows. We agree on the role model and client interface before the start.
Frequently asked questions about AI engineers
AI engineer or machine learning engineer: what is the difference?
The terms overlap. AI engineers bring AI into existing systems: LLM applications, RAG, agents and integration. Machine learning engineers (ML engineers) train, run and monitor models, often with MLOps tools such as MLflow. Before the start, we clarify which profile your project needs and combine both roles if required.
How quickly can an AI engineer start?
Within 14 days of your approval. We present concrete proposals within 72 hours, depending on platform, seniority and availability.
Which platforms do your AI engineers work with?
Azure OpenAI and Azure AI Foundry, Amazon Bedrock and Amazon SageMaker, Google Vertex AI, frameworks such as LangChain and LlamaIndex, vector databases, and MLflow and Kubeflow for operations. Further platforms by agreement.
Who is my contract partner?
Your contract partner is Delvera GmbH in Munich. The engagement is coordinated through Delvera instead of you managing several contracts with international partners.
Who steers the project after the start?
Technical steering and acceptance stay with you. Delvera remains your central point of contact for alignment, communication, change requests and escalation.
What happens if the fit is not right?
If the technical or communication fit is not right, we first identify the cause together with you and the specialist and agree on concrete improvement measures. If there is no reliable improvement within two weeks, we initiate a replacement.
More roles
- ServiceNowServiceNow consultants and developers, as freelancers in Germany or as nearshore capacity from our partner network.
- Data EngineerData platforms, pipelines and integration for ongoing data projects.
- DevOps EngineerCI/CD, Kubernetes and infrastructure automation.
- Cloud EngineerAWS, Azure and GCP: migration, landing zones and infrastructure as code.
- Software DevelopersJava, .NET, React, Python, mobile and test automation.
- SAP ConsultantsS/4HANA, BTP, ABAP and Fiori: consulting and development.
Related models and locations: Team Extension · Nearshore
Intro call: role, location model, budget, start date.
We clarify role, skill requirements, location model, budget and start date. Within 72 hours you receive 3-5 concrete proposals - or an honest assessment if we're not the right fit.
- 3-5 concrete proposals within 72 hours
- Budget range before we introduce profiles
- No obligation, no sales pressure
Or directly by email: info@delvera.de