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Hire AI Developers for Production GenAI Features

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.

Proof of AI Delivery You Can Validate

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.

When Teams Hire AI Developers

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

What Our AI Developers Deliver

We embed AI engineers who can deliver end-to-end: data → retrieval → model integration → product UX → evaluation → monitoring.

RAG Assistants and Knowledge Search

  • Retrieval pipelines connected to your docs, wikis, tickets, PDFs, and databases
  • Chunking and indexing tuned for relevance and answer quality
  • Grounding patterns (citations/traceability, confidence signals where used)
  • Secure retrieval with role-based access controls for enterprise-safe RAG

AI Agents and Workflow Automation

  • Agent workflows for task execution (triage, routing, summarization, actions)
  • Tool/function calling patterns to interact with internal systems safely
  • Guardrails to keep agents bounded and predictable
  • Human-in-the-loop approvals where automation needs control

LLM Integration Into Your Product

  • Feature design aligned to real user workflows (not "chat for everything")
  • Structured outputs (schemas/JSON) for reliable downstream use
  • Multi-model strategies where cost, latency, or quality varies by use case
  • Integration into existing authentication, permissions, and audit needs

Evaluation, Quality, and Risk Controls

  • Test sets aligned to real user questions and workflows
  • Metrics: relevance, factuality/grounding, refusal correctness, latency, cost
  • Regression testing across prompt changes and retrieval updates
  • Safety filters and policy-aligned controls for customer-facing AI

Monitoring, Feedback Loops, and Improvement

  • Production monitoring for failures, drift, and low-quality outputs
  • Feedback collection and review workflows for continuous improvement
  • Iteration loops for prompts, retrieval, and source data quality
  • Reliability improvements over time—not one-off prompt tuning

AI Developers by Capability

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)

AI Tooling Coverage

This section supports "developer by tooling" searches while staying readable.

Orchestration and Application Frameworks (Where Used)

  • LangChain developers for orchestration patterns (where adopted)
  • LlamaIndex developers for retrieval/indexing frameworks (where adopted)
  • Engineers who can implement orchestration without heavy frameworks when simplicity is better

Vector Search and Retrieval Developers

  • Pinecone developers / Weaviate developers / Milvus developers (where used)
  • pgvector developers for Postgres-based vector search
  • Hybrid retrieval patterns (keyword + vector) where needed

Data Pipelines and Ingestion Developers

  • Ingestion from docs, wikis, tickets, CRM, databases
  • Cleaning, chunking, metadata strategy, incremental refresh pipelines
  • Access control alignment so RAG respects permissions

Evaluation and Observability Developers

  • Evaluation harnesses and regression suites for AI behavior
  • Monitoring patterns for quality drift, cost, latency, and failures
  • Review workflows for escalations and human oversight

Integration and Delivery Developers

  • Python developers for AI engineering and pipelines
  • Node.js/TypeScript developers for product integration in web ecosystems
  • Streaming responses, structured outputs, and API-first AI feature delivery

How We Work Inside Your Team

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

Engagement Options

Dedicated AI Developer

Best for an ongoing GenAI roadmap with continuous iteration.

AI Specialist

Best for RAG architecture, evaluation setup, or stabilizing a prototype before production.

Pod-Based AI Delivery

For faster production outcomes: AI Developer + Backend Developer, AI Developer + QA, AI Developer + UI/UX (workflow-first AI experiences).

Common Outcomes Teams Expect

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

Frequently Asked Questions

Common questions about hiring AI developers

Do you build production AI or just demos?

Production is the focus—evaluation, monitoring, guardrails, and reliable integration are part of delivery.

Can you build RAG using our internal documents and knowledge bases?

Yes. RAG assistants grounded in internal docs are one of the most common engagements.

How do you reduce hallucinations?

We focus on grounding (retrieval), structured outputs, evaluation/regression testing, and guardrails—not just prompt tweaks.

Can we start with one AI developer and scale later?

Yes. Many teams start with one AI developer and scale into a pod as usage grows.

Ship an AI Capability You Can Trust in Production

If your AI feature needs to be reliable, permission-aware, and measurable—not just impressive in a demo—we'll embed AI developers who can build and harden the system inside your product.

Your Vision, Our Expertise

Tell us about your needs, and we’ll build the right solution for you.

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