01 / CAPABILITY EXAMPLE
GenAI Engineer
8+ years
Production focus
RAG systems · Agent workflows · Evaluation
Expertise
PythonLangGraphOpenAIVector DBAWS
Industry context · Financial services · Logistics
Discuss this capabilityAI Talent / The engineering talent layer
AI roadmaps are everywhere.
People who can ship them aren't.
Access 10,000+ engineers across AI, data, cloud, enterprise applications and modern engineering — with specialists who have worked hands-on with production AI systems.
AI
DATA
CLOUD
BACKEND
ENTERPRISE APPS
DEVOPS
QUALITY
SECURITY
10,000+Engineers
10,000+ENGINEER NETWORK
50+Technologies
ProductionAI experience
GlobalDelivery
Beyond AI skills
AI-trained ≠ AI battle-tested.
01 / Foundation
AI-Trained
- Courses
- Certifications
- Prompt engineering
- Sandbox projects
- Model APIs
- Proofs of concept
- Tutorial architectures
02 / Execution
Battle-Tested
- Production AI systems
- Enterprise integrations
- Real customer data
- Security & governance
- Latency & cost optimisation
- Observability
- Model evaluation
- Failure handling
- Scale
- Human-in-the-loop workflows
- Legacy system integration
- Production deployment
We don't screen for who knows AI.
We screen for who has shipped it.
The Synoria network
10,000+ engineers.
One engineering ecosystem.
AI doesn't ship through AI engineers alone. Production systems need data, backend, cloud, enterprise applications, quality and security working as one engineering system.
Synoria10,000+Engineers
01 / Data & AI
AI
Engineering roles
GenAI EngineersAI / ML EngineersAgentic AI EngineersData ScientistsData EngineersMLOps EngineersAI Architects
Technology nodes
OpenAIAnthropicLlamaLangChainLangGraphDatabricksVector DatabasesSQL / NoSQLPower BI
AI engineering is an ecosystem — not a job title.
50+ technologies
Whatever your stack.
Add the expertise.
01Enterprise applications
SAPSalesforceServiceNowOracleMicrosoftDynamics 365
02Programming
PythonJava.NET / C#GoJavaScript
03Cloud & DevOps
AWSAzureGCPDockerKubernetesTerraformCI/CD
04Data & AI
DatabricksOpenAIAnthropicLlamaLangChainLangGraphVector DatabasesSQL DatabasesNoSQL DatabasesPower BI
05Specialist capabilities
Business AnalysisQA & AutomationCybersecurityArchitectureProduct Engineering
Production AI
An AI engineer isn't enough.
Enterprise AI touches models, applications, data, cloud infrastructure, security and the systems your business already runs.
01 / Business systemsSAP · Salesforce · ServiceNow · Dynamics · Oracle
02 / Application engineeringJava · .NET · Python · JavaScript · Go
03 / AI application layerAgents · RAG · GenAI · Computer Vision · Predictive AI
04 / AI / model layerOpenAI · Anthropic · Llama · LangGraph · LangChain
05 / DataDatabricks · Databases · Vector Databases
06 / Cloud & platformAWS · Azure · GCP · Kubernetes
07 / Quality & governanceQA · Security · Observability · MLOps
This is why our network extends far beyond AI specialists.
How we validate talent
We select for the initiative.
Not the keyword list.
Capability that integrates. Expertise that transfers.
01 →
Experience mapping
What have they actually built?
02 →
Technical screening
Can they solve real engineering problems?
03 →
AI depth
Models · RAG · Agents · Data · Evaluation
04 →
Production validation
Scale · Security · Cost · Reliability
05 →
Enterprise readiness
Cloud · Integrations · Governance · Collaboration
06 →
Initiative match
Experience matched to the actual problem.
Multidimensional screening
A resume tells you the technologies.
We map the engineering depth.
Illustrative capability mapping: sample roles and intensity levels show how depth can be evaluated, not actual individual assessment results.
| Example discipline | LLMs | RAG | Agents | Python | Data | Cloud | MLOps | Integration | Production AI |
|---|---|---|---|---|---|---|---|---|---|
| GenAI EngineerExample · 8 yrs | |||||||||
| AI ArchitectExample · 12 yrs | |||||||||
| ML EngineerExample · 7 yrs | |||||||||
| Data EngineerExample · 9 yrs | |||||||||
| Backend EngineerExample · 8 yrs | |||||||||
| MLOps EngineerExample · 10 yrs |
Intensity: ■ Expert · Advanced · Experienced · Exposure
The people behind the network
See the kind of engineers you can deploy.
Illustrative capability archetypes, not verified individual profiles, deployment counts or availability commitments.
02 / CAPABILITY EXAMPLE
AI Solution Architect
12+ years
Production focus
Enterprise GenAI · Agentic architecture
Expertise
AzureOpenAIPythonDatabricksMicroservices
Industry context · Healthcare · Enterprise
Discuss this capability03 / CAPABILITY EXAMPLE
Data Engineer
9+ years
Production focus
RAG pipelines · Enterprise AI data platforms
Expertise
DatabricksPythonSparkAzureData Lakes
Industry context · Enterprise · Financial services
Discuss this capability04 / CAPABILITY EXAMPLE
Agentic AI Engineer
7+ years
Production focus
Multi-agent workflows · Enterprise automation
Expertise
LangGraphLangChainPythonAnthropicTool Calling
Industry context · Operations · Technology
Discuss this capability05 / CAPABILITY EXAMPLE
MLOps / Platform Engineer
10+ years
Production focus
Model deployment · Observability · Scaling
Expertise
AWSKubernetesDockerMLflowCI/CD
Industry context · Enterprise · Manufacturing
Discuss this capability06 / CAPABILITY EXAMPLE
Enterprise Integration Engineer
10+ years
Production focus
Enterprise AI integration · API architecture
Expertise
JavaAPIsSAPSalesforceAzure
Industry context · Enterprise · Retail
Discuss this capabilityAI team anatomy
One AI initiative.
More than one kind of engineer.
A production AI product needs interlocking engineering disciplines. Select a role to see its contribution.
AI product / outcome
Role / 02
GenAI Engineer
Builds agents, retrieval and evaluation workflows.
Python · LangGraph · OpenAI · Anthropic
One specialist to a complete team
One specialist can become a complete AI team.
Keep the context as the initiative grows. Same engineering network. Different level of ownership.
Stage 01
Add capability
1–2 specialists
Staff Augmentation
Stage 02
Add scale
5–10 specialists
Dedicated AI Team
Stage 03
Add ownership
Cross-functional pod
Managed delivery
Stage 04
Add outcome
End-to-end delivery
Fixed-Bid AI Project
Same engineering network. Different level of ownership.
From requirement to deployment
The right expertise.
Without restarting the hiring machine.
No unsupported promise about an exact time to deploy: the right path depends on the initiative and client validation.
Traditional hiring
Source
Screen
Technical interview
Multiple rounds
Offer
Notice period
Join
Ramp up
Synoria network
Define requirement
Capability mapping
Curated engineers
Client validation
Deploy
Scale
Why experience matters
Building the demo is the easy part.
The talent required at enterprise scale is fundamentally different from the talent required for an experiment.
01 / Maturity
Experiment
Prompting · Model APIs · Prototype
02 / Maturity
POC
RAG · Agent workflow · Application
03 / Maturity
Production
Security · Evaluation · Monitoring · Data pipelines · Reliability
04 / Battle-tested engineering
Enterprise scale
Governance · Cost · Performance · Integration · Observability · Change management
Industry context
Technology depth.
Business context.
| Industry | GenAI | Agents | RAG | Computer Vision | Predictive AI | Automation | Digital Twins |
|---|---|---|---|---|---|---|---|
| Financial Services | — | Credit decisioning | Research assistant | — | — | — | — |
| Healthcare | — | — | Clinical knowledge | — | — | — | Hospital operations |
| Manufacturing | — | — | — | Quality inspection | — | — | Production optimisation |
| Logistics | — | Supply chain control tower | — | — | ETA intelligence | — | — |
| Real Estate | — | — | — | Construction intelligence | — | — | Portfolio twin |
| Retail | Product discovery | Merchandising agent | — | — | — | — | — |
| Travel | — | Airline recovery | — | — | Aircraft maintenance | — | — |
| Enterprise | — | — | Knowledge GraphRAG | — | — | Operations agent | — |
10,000+
Engineers in the network
50+
Technology capabilities
43
AI use cases showcased
Global
Delivery capability
Build your AI capability
Tell us what you're trying to build.
We'll bring the people who can ship it.
From one specialist to a complete cross-functional AI team, access engineering capability matched to your technology, industry and delivery model.
Staff AugmentationDedicated AI TeamsFixed-Bid Delivery