Hire in days, not months

Hire AI Developers

Integrate production-grade AI into your product with engineers who focus on accuracy and cost.

From RAG architecture to custom AI agents, our AI developers help you leverage LLMs to automate workflows, improve user experiences, and reduce operational costs with predictable delivery.

Get Matched with AI Talent

AI delivery governance

Governance built for reliable and secure AI releases

Reduce AI risk with explicit evaluation controls, data privacy standards, and cost monitoring tailored to LLM-powered products.

Controls teams ask for before AI launch

Accuracy, security, and cost discipline mapped to how modern AI stacks actually ship.

Shortlist turnaround

4.2 days median across recent AI roles

Kickoff speed

10 days median from selection to sprint start

Accuracy retention

96% of AI features meet quality benchmarks after 90 days

AI evaluation and quality guardrails

Automated testing for hallucinations, bias, and accuracy to ensure AI responses stay within brand guidelines.

Quality control

Data privacy and security compliance

Secure handling of PII, prompt injection protection, and compliance with data residency requirements for AI workloads.

Security-ready

Token and cost monitoring

Real-time tracking of API usage and cost optimization strategies to prevent budget overruns.

Cost-aware

Talent pool preview

Vetted AI Developer profiles ready to interview

Review a balanced shortlist with specialist, senior, and principal depth so you can hire for immediate delivery and long-term technical leadership.

View more profiles

Ali T.

Senior AI Developer

Vetted

7 years

Role-matched

PythonLangChainRAGPinecone

Built an AI-powered customer support assistant for a SaaS platform, reducing human ticket volume by 45% while maintaining a 92% customer satisfaction score.

Ahsan I.

AI Developer

Vetted

5 years

Role-matched

OpenAI SDKTypeScriptSemantic SearchFastAPI

Implemented a semantic product search engine for a large ecommerce site, increasing search-to-cart conversion rate by 18% through better intent matching.

Muhammad X.

Principal AI Developer

VettedArchitect

10 years

Role-matched

LLMOpsModel EvaluationSecuritySystem Design

Developed a secure AI document analysis tool for a fintech firm, automating 70% of compliance reviews with strict data privacy and hallucination controls.

Need a wider shortlist?

We can share additional ai developer profiles by seniority, timezone, and domain fit.

AI engagement options

Choose the engagement model that matches your AI roadmap

Start with focused AI experiments or scale to a full AI engineering pod as your product evolves.

Model selection support

We map AI role shape to roadmap pressure, data complexity, and stakeholder expectations.

Part-time AI support

Best for AI experiments, prompt optimization, and ongoing RAG improvements.

Starts from $2,000 / month

Best for: Iterative AI improvements and feasibility testing

20-25 hrs/week
AI sprint support
Weekly accuracy reporting

Large-scale data engineering and model training are scoped separately.

Get Started

Full-time AI engineer

Recommended

Best for core AI feature delivery with daily ownership and production momentum.

Starts from $4,000 / month ($25/hour)

Best for: Active AI roadmap execution and product integration

40 hrs/week
Full AI ownership
Daily accuracy + cost updates

AI API costs and third-party platform fees are billed separately.

Get Started

AI engineering pod (2 AI Devs + 1 Data Eng + 1 PM)

Best for complex AI products, multi-agent systems, and large-scale RAG infrastructure.

Starts from $15,000 / month

Best for: High-stakes AI initiatives with significant data and coordination needs

Cross-functional AI pod
Parallel AI workstreams
End-to-end AI orchestration

Specialized AI security audits are scoped separately.

Get Started

AI hiring process

From AI feasibility to production contribution in under two weeks

The process is tuned for AI delivery risk: model selection, RAG accuracy, prompt safety, and cost management.

Typical kickoff window

Most teams start AI delivery with selected talent in 7-14 days.

Decision points are explicit: AI implementation depth, evaluation discipline, and cost-awareness are validated before kickoff.

1
AI goal alignment and data audit
Step 1
We map your AI objectives, data availability, and accuracy requirements to define role scope and success metrics.
Day 1-2
2
Shortlist with relevant AI project context
Step 2
Review candidates with prior experience in similar LLM use cases, RAG patterns, or agent architectures.
Day 2-5
3
Technical validation with AI scenarios
Step 3
Interviews test prompt engineering logic, RAG retrieval strategies, and AI cost/performance tradeoff handling.
Day 5-10
4
Onboarding and AI sprint integration
Step 4
Selected engineers join your workflows with clear AI ownership and immediate first-sprint goals.
Day 7-14

Why product teams hire us for AI

AI execution tuned for accuracy, speed, and operational reliability

You get engineers who can build production-grade AI features without the overhead of a traditional data science team.

Built for high-stakes AI delivery

Designed for teams shipping user-facing AI, internal automation, and data-driven discovery tools.

Typical start

10 days median to sprint kickoff

Accuracy lift

35% median improvement in RAG relevance

Cost reduction

24% median reduction in AI API spend

Fast AI prototyping and iteration

Move from AI concept to working prototype quickly using modern LLM frameworks and tools.

Velocity

Focus on AI accuracy and reliability

Engineers prioritize evaluation and guardrails to ensure AI features are safe for production.

Reliability

Cost-optimized AI implementation

Delivery decisions account for token usage, model costs, and long-term operational efficiency.

Efficiency

Service scope

AI capabilities mapped to business impact, not just model calls

Use this service scope to match your AI roadmap to the right implementation pattern, whether you need internal automation, user-facing features, or scalable AI infrastructure.

LLM and Generative AI

Custom LLM application development

Our AI developers build applications powered by OpenAI, Anthropic, or open-source models (Llama, Mistral) tailored to your specific business logic and user requirements.

RAG (Retrieval-Augmented Generation) systems

Hire AI engineers to build knowledge-aware systems that query your private data using vector databases like Pinecone or Weaviate for accurate, context-rich responses.

AI agents and autonomous workflows

Develop multi-agent systems using LangChain or AutoGPT that can perform complex tasks, research, and data processing with minimal human intervention.

Integration and Optimization

AI-powered product features

Integrate smart features like automated summarization, sentiment analysis, content generation, and intelligent search directly into your existing SaaS or web products.

Prompt engineering and model tuning

Optimize LLM performance through advanced prompt engineering, few-shot learning, and fine-tuning on domain-specific datasets to improve accuracy and reduce latency.

AI cost and performance optimization

Reduce API costs and improve response times by implementing caching strategies, model selection logic, and efficient token management.

Data and Infrastructure

Vector database architecture

Design and implement scalable vector search infrastructure for semantic search, recommendation engines, and long-term AI memory.

AI pipeline automation (LLMOps)

Build automated pipelines for data ingestion, embedding generation, model evaluation, and deployment to ensure reliable AI performance in production.

Semantic search and discovery

Replace traditional keyword search with intent-aware semantic search that understands user queries and returns more relevant results.

Engineering stack

Production-ready AI stack for accuracy, speed, and cost efficiency

Stack choices are optimized for fast AI iteration, model reliability, and scalable data processing across RAG, agents, and integrated AI features.

OpenAI SDK

LangChain

LlamaIndex

Hugging Face

Python

TypeScript

Node.js

FastAPI

Pinecone

Weaviate

ChromaDB

Milvus

PyTorch

TensorFlow

Pandas

Scikit-learn

AWS (Bedrock/SageMaker)

GCP (Vertex AI)

Docker

GitHub Actions

Hiring readiness

AI hiring playbook: evaluate quickly and onboard with less risk

Use this decision hub to align AI interview depth, set accuracy boundaries, and connect hiring to measurable AI outcomes.

Role scope Interview playbook Business impact

Responsibilities / Role Scope

Owns

AI feature implementation with focus on accuracy and reliability
RAG pipeline optimization and vector search performance
Prompt engineering and model evaluation frameworks
AI-related API integrations and data processing workflows

Collaborates on

Product teams to define AI feasibility and user value
Backend engineers for secure data access and API contracts
UX designers to create intuitive AI-driven interfaces
DevOps for model deployment, monitoring, and cost tracking

Interview Questions

Structured by level for consistent and faster interviewer calibration.

junior

Fundamentals and execution reliability

What is the difference between a prompt and a completion?
How does a vector database help in a RAG system?
What are tokens, and why do they matter for AI costs?
How do you handle basic error cases in LLM API calls?

mid

Delivery ownership and decision quality

How do you design a RAG pipeline for high retrieval accuracy?
What techniques do you use to reduce LLM hallucinations in production?
How do you implement streaming responses for better user experience?
How do you evaluate the quality of AI-generated content at scale?
When should you use fine-tuning versus RAG for a specific use case?

senior

Architecture, risk control, and leadership

How do you architect a multi-agent system for complex task decomposition?
How do you manage long-term memory and context windows in AI agents?
How do you design a scalable LLMOps pipeline for model monitoring and versioning?
How do you handle security risks like prompt injection in user-facing AI tools?
How would you optimize a high-traffic AI feature for both latency and cost?

Why Outsource This Role

Faster AI deployment

Ship AI-powered features without the steep learning curve or hiring delays of building an in-house ML team.

Median kickoff: 10 days from role approval

AI cost efficiency

Leverage AI-assisted delivery and optimized model selection to reduce total development and operational costs.

Starts from $25/hour for AI engineering

Improved accuracy

Reduce hallucinations and improve response relevance with engineers who specialize in RAG and evaluation.

Median accuracy lift: 35% in 6 weeks

Reduced AI risk

Implement guardrails, security checks, and evaluation layers to protect your brand and user data.

Hallucination rate reduced 40% quarter-over-quarter

Scalable AI bandwidth

Start with focused AI experiments and scale to full-scale production systems as value is proven.

AI pod scale-up window: 2-3 weeks

Benefit	Outcome	Metric
Faster AI deployment	Ship AI-powered features without the steep learning curve or hiring delays of building an in-house ML team.	Median kickoff: 10 days from role approval
AI cost efficiency	Leverage AI-assisted delivery and optimized model selection to reduce total development and operational costs.	Starts from $25/hour for AI engineering
Improved accuracy	Reduce hallucinations and improve response relevance with engineers who specialize in RAG and evaluation.	Median accuracy lift: 35% in 6 weeks
Reduced AI risk	Implement guardrails, security checks, and evaluation layers to protect your brand and user data.	Hallucination rate reduced 40% quarter-over-quarter
Scalable AI bandwidth	Start with focused AI experiments and scale to full-scale production systems as value is proven.	AI pod scale-up window: 2-3 weeks

Client stories

Trusted by teams that ship fast

Real feedback from partnerships where we embedded with product teams, accelerated delivery, and stayed accountable to outcomes.

“Their contribution went beyond coding. They helped us improve estimation, tighten acceptance criteria, and establish a delivery rhythm that made planning more predictable. As a result, we hit our launch date with fewer surprises and had a cleaner backlog going into the next quarter.”

Michael T.

VP Product, B2B SaaS

“Our biggest concern was scalability during a period of rapid growth, and their team handled it with confidence. They refactored key backend services, introduced safer deployment practices, and helped us scale traffic without downtime during peak usage windows. We saw immediate performance gains and far fewer late-night incidents.”

Sarah K.

Engineering Manager, Enterprise Platform

“We needed to launch a new product line on a fixed deadline, and missing it would have impacted revenue. Codexty helped us reorganize delivery, close technical gaps, and execute with steady weekly progress updates. We shipped ahead of schedule and exceeded our initial activation targets in the first month.”

Lisa R.

Operations Director, Logistics Company

FAQ

Answers to practical decision questions before you hire.

How quickly can an AI developer start?

Most AI projects begin onboarding within 7-14 days after role alignment and interview completion.

Do you work with OpenAI and LangChain?

Yes. We regularly build AI applications using OpenAI, Anthropic, LangChain, LlamaIndex, and various vector databases.

Can you help with RAG and vector search?

Yes. We specialize in building RAG pipelines with Pinecone, Weaviate, and ChromaDB for high-accuracy retrieval.

How do you handle AI hallucinations?

We implement multi-layer evaluation, factual checks, and prompt guardrails to minimize hallucinations and ensure reliable output.

Do you build custom AI agents?

Yes. We develop autonomous and semi-autonomous agents for task automation, research, and complex data processing.

What is the hourly rate for AI developers?

Our AI engineering services start at $25/hour, providing high-quality delivery at a competitive rate.