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Add engineers who can turn a generative-AI use case into a production feature—connecting models, enterprise knowledge, prompts, structured outputs, evaluations, security, observability and accountable handoff.
16+ Years of AI & Software Delivery
250+ Engineers Across AI, Data, Cloud, Security & Product Engineering
U.S.-Led Project Management | Global Delivery
Hire generative AI developers for a defined model, RAG, integration or evaluation gap—or for ownership across application architecture, data, behavior, deployment and improvement. Looking for managed, end-to-end delivery instead of dedicated hiring? See our generative AI development services. Need retrieval-augmented grounding or enterprise knowledge systems built as a managed project? Explore our RAG system development services, or our LLM development services for custom model work. Need broader AI talent beyond generative AI engineering? Hire AI developers, hire AI agent developers for agent-specific workflows, or hire MLOps engineers for model deployment and lifecycle work. For the surrounding application, see our custom software development services.
Translate one use case into users, inputs, outputs, model constraints, data flows, success measures, risk tiers and the simplest architecture that can work.
Build model-backed application features with provider APIs or approved open models, structured outputs, streaming, caching, fallbacks and enterprise integrations.
Design ingestion, chunking, metadata, retrieval, reranking, citations and access-aware answers grounded in approved business knowledge.
Engineer instructions, examples, context assembly and text, image, audio or document inputs with versioned behavior and clear output contracts.
Build task datasets, graders, red-team cases, policy checks, human review, quality/cost/latency monitoring, regression gates and incident procedures.
Deploy to the approved stack, tune model and retrieval choices, document controls, transfer ownership and establish a measured improvement backlog.
Our generative AI developers bring proven experience across LLM application architecture, prompt and context engineering, RAG and knowledge grounding, multimodal workflows, evaluation and guardrails, and observability and deployment.
Select model providers and design the application architecture around structured outputs, streaming, caching and fallbacks.
Engineer instructions, examples, context assembly and versioned behavior with clear output contracts.
Design ingestion, chunking, retrieval, reranking and citations grounded in approved business knowledge.
Work across text, image, audio and document inputs with validated, bounded output contracts.
Build task datasets, graders, red-team cases, policy checks and human review before expanding scope.
Deploy to the approved stack with monitored cost, latency and quality, and a documented handoff.
Review a representative role profile, then request two or three current CVs matched to your use case, model providers, application stack, enterprise systems, retrieval needs, data sensitivity, deployment environment, evaluation criteria and working-hour overlap.
Keep each item visibly labeled “Solution Blueprint” until DreamzTech verifies the client, production status, contribution, evidence, outcome and permission to publish.
Environment: internal documents and support
Core: LLM, hybrid retrieval, reranking, citations
Answer from permission-aware business knowledge and abstain or escalate when evidence is missing. Accept on retrieval coverage, citation correctness, supported-answer rate, access isolation, latency and cost.
Environment: operations and compliance
Core: templates, structured outputs, policy retrieval, human review
Draft a structured document from approved inputs, show source evidence and route exceptions for review. Accept on field accuracy, format validity, policy conformance, reviewer edits, privacy checks and audit evidence.
Environment: image and text intake
Core: multimodal model, validation, application APIs, eval set
Interpret approved images and text, produce a bounded result and preserve human approval for consequential use. Accept on task accuracy, unsafe-input handling, output validation, fallback behavior, latency and unit cost.
Simple & Transparent Pricing | Fully Signed NDA | Code Security | Easy Exit Policy
Begin with the user outcome, representative inputs, required evidence, sensitive data, current systems and failure tolerance. Share prototypes, known errors and the architecture your team must own.
Share your generative AI use case and requirements and we will design the fastest path to a controlled, production-ready deployment.
Our generative AI developers bring proven experience across LLM application architecture, prompt and context engineering, RAG and knowledge grounding, multimodal workflows, evaluation and guardrails, and observability and deployment.
| Languages & Application Engineering | PythonTypeScript/JavaScriptJavaC#GoSQLthe client’s application framework |
| Models & Provider Platforms | OpenAIAnthropicGoogle Gemini/Vertex AIAzure AIAmazon Bedrockapproved open-weight models |
| Prompt & Context Engineering | Versioned promptsexamplestemplatesstructured outputscontext assemblycachingprovider SDKs |
| Application Frameworks & Integrations | FastAPINode.jsREST/GraphQLwebhooksqueuesCRMERPcollaboration toolscustom connectors |
| Retrieval & Knowledge | Embeddingschunkinghybrid searchrerankingPineconeWeaviatepgvectorElasticsearch/OpenSearchgoverned ingestion |
| Data, State & Storage | PostgreSQLobject storageRedisdocument storesmetadata catalogsaccess-aware session state |
| Evaluation & Testing | Golden datasetstask-level gradersmodel/tool mocksadversarial casesregression suitesprovider evaluation tools |
| Safety, Security & Governance | Data minimizationleast privilegesecretstenant isolationinput/output controlsPII handlinghuman reviewaudit logsred teaming |
| Observability & Operations | Tracesstructured logsOpenTelemetryLangSmith or equivalentlatency/token/cost dashboardsalertsrollback |
| Cloud, Containers & Delivery | AWSAzureGoogle CloudDockerKubernetesserverless runtimesCI/CDinfrastructure as code |
| Multimodal Experience & Channels | Web/mobilechatdocumentsimagesaudio/voiceemailSlack/Teamsreview queuesaccessible interfaces |
Hire dedicated AI developers for your project with a quick, efficient hiring process. Build your AI engineering capacity faster with matched, evaluated talent.
Tell us the generative AI use case to build, the data and systems it needs, and the deployment environment and quality bar you require.
Review matched generative AI developer profiles and interview candidates on architecture, evaluation approach and delivery experience.
Confirm scope, access and onboarding readiness—including contracting, data/system access, security review and a realistic start date.
Hire generative AI developers who deliver secure, well-evaluated generative AI capabilities across a wide range of industries.
Manufacturing
Logistics
Retail
eLearning
Fintech
Agriculture
Travel
Casino
Sports
Healthcare
Real Estate
Facility
Production GenAI crosses software, data, cloud, security, UX and operations. DreamzTech can match the core developer and connect adjacent specialists when the use case crosses role boundaries.
Share the use case, systems, data boundaries and production goals. We will respond with the likely developer profile, readiness questions and a practical first scope.
Got questions about hiring generative AI developers? Explore the FAQs below to learn how DreamzTech matches generative AI developers to your use case, stack and risk requirements.
A generative AI developer builds software that creates or transforms text, images, audio, code or structured data using foundation models. Production work usually includes use-case design, model and provider selection, prompt and context engineering, RAG, structured outputs, integrations, evaluations, safety controls, observability, deployment, cost control and handoff—not only prompt writing.
Use RAG when answers need current, private or frequently changing knowledge and users benefit from citations. Consider fine-tuning when you need repeatable style, format or task behavior that prompting and examples cannot deliver efficiently. They can be combined, but start with a measured baseline and choose the smallest intervention that improves the target evaluation set.
They build evaluation sets from real tasks, edge cases and known failures, then measure retrieval, factual support, citation quality, format validity, task success, safety, latency and cost. Ground outputs in approved sources, require structured output where possible, allow abstention or escalation, use human review for consequential decisions, and rerun regression tests whenever models, prompts or data change.
They minimize data sent to models, classify sensitive inputs, enforce tenant and role boundaries, protect secrets, validate inputs and outputs, restrict connectors, log approved events and define retention and deletion behavior. Provider settings, hosting choices and contracts must be reviewed for the specific data. NIST and OWASP guidance supports threat modeling, testing, monitoring and accountable human oversight.
Generative AI creates or transforms content from an input. An AI agent uses a model inside a workflow that can choose steps, call tools, maintain state or act toward a goal. A product may use generative AI without agent behavior, while an agent often uses generative models. Hire for the actual workflow, integrations and risk—not the label alone. Need agent-specific engineering instead? See our hire AI agent developers page.
Yes, when the systems expose suitable APIs, events, databases or approved connectors. Match the developer to your application stack, cloud, identity model, data stores, search layer and preferred providers. Define ownership, environments, access, testing and fallback behavior before onboarding so the new capacity strengthens the existing delivery process.
Cost depends on seniority, use-case complexity, RAG and data work, integrations, evaluation depth, security, deployment, support and working-hour overlap. DreamzTech may publish $20 per hour or $3,200 for a 160-hour monthly allocation only after sales confirms applicability. Model/API usage, cloud, search/vector services, licenses and extended support are separate unless included by contract.