Hire AI developers to ship production GenAI features—RAG assistants, agents, and LLM integrations built for real workflows, evaluation, and monitoring. Our developers embed into your team so AI moves from prototype to reliable product capability.
AI success is measurable when it's tied to quality, reliability, and business outcomes—not just a demo.
Case snapshots, testimonials, and before/after evaluation results will be displayed here.
Teams typically hire AI developers when:
You want to add GenAI into an existing product without breaking user trust
You need a RAG assistant connected to internal documentation or customer data
You're building agents to automate internal workflows (support, ops, sales enablement)
Hallucinations, wrong answers, or inconsistency are blocking release
You need evaluation, guardrails, and monitoring before shipping
Your team lacks AI engineering capacity but has clear product use cases
You need to move from prototype → production with correct architecture
We embed AI engineers who can deliver end-to-end: data → retrieval → model integration → product UX → evaluation → monitoring.
Many buyers search by capability because the problem is already defined. We staff AI developers across common GenAI delivery needs:
GenAI developers for customer-facing and internal AI product features
LLM developers for model integration, routing, and reliability patterns
RAG developers for grounded answers using your knowledge sources
AI agent developers for tool-using workflows and controlled automation
Machine learning developers for classical ML components where needed (ranking, classification)
This section supports "developer by tooling" searches while staying readable.
Work in your Jira/Linear/Azure DevOps workflow
Collaborate in Slack/Teams with product, engineering, and domain owners
Build in your repo with PR reviews and release standards
Align to your Definition of Done (evaluation, monitoring, safety checks)
Deliver in sprint-based or weekly cadence with visible progress
Document decisions so AI capability is maintainable long-term
Best for an ongoing GenAI roadmap with continuous iteration.
Best for RAG architecture, evaluation setup, or stabilizing a prototype before production.
For faster production outcomes: AI Developer + Backend Developer, AI Developer + QA, AI Developer + UI/UX (workflow-first AI experiences).
A production-ready RAG assistant grounded in your knowledge sources
Reduced hallucinations through retrieval, evaluation, and guardrails
AI features integrated into real workflows (not standalone chat)
Measurable quality improvements through test sets and monitoring
Lower support load through safe automation and better information access
Common questions about hiring AI developers
Production is the focus—evaluation, monitoring, guardrails, and reliable integration are part of delivery.
Yes. RAG assistants grounded in internal docs are one of the most common engagements.
We focus on grounding (retrieval), structured outputs, evaluation/regression testing, and guardrails—not just prompt tweaks.
Yes. Many teams start with one AI developer and scale into a pod as usage grows.
Tell us about your needs, and we’ll build the right solution for you.
© SiGi 2014-2025. All rights reserved
© SiGi 2014-2025. All rights reserved