Hire Remote AI Developers

Hire Senior AI Developers Who Build AI That Generates Real Revenue

There’s a difference between a developer who’s fine-tuned a model in a Jupyter notebook and one who’s shipped an AI system into production — with proper error handling, observability, fallback logic, cost management, and the feedback loops that make AI systems improve over time. The former is common. The latter is rare, and they’re on our bench.

Our AI developers have built production AI systems that generated $50M+ in revenue for Fortune 500 companies and unicorn startups — AI-powered customer service automations, LLM-driven search and discovery systems, computer vision quality control, multi-agent financial analysis, and generative AI applications that reached tens of millions of users.

What Our AI Developers Build

LLM-Powered Applications

GPT-4, Claude, Gemini, Llama, and Mistral — our AI developers build the application layer that turns raw language model capabilities into products that solve real business problems. Prompt engineering, context management, structured output parsing, tool use, and function calling — all implemented with the reliability and observability that production requires.

RAG Systems & Enterprise Search

Retrieval-augmented generation (RAG) pipelines that connect your company’s proprietary knowledge to LLMs. Our AI engineers design and implement the document ingestion pipelines, vector embedding strategies, chunk optimization, and hybrid search architectures that make enterprise RAG systems accurate and fast.

AI Agents & Multi-Agent Systems

Autonomous AI agents built with LangChain, LangGraph, AutoGen, or custom orchestration — capable of planning, tool use, memory, and multi-step reasoning. Our AI developers build the agentic systems that automate complex workflows that were previously impossible to automate.

Computer Vision Systems

Object detection, image classification, segmentation, OCR, and video analysis systems built on PyTorch, TensorFlow, and ONNX. Our AI engineers have built production computer vision systems for manufacturing quality control, medical imaging, retail analytics, and autonomous systems.

AI Integration & API Development

FastAPI and Python-based AI service APIs that expose model capabilities to the rest of your product. Our AI developers build the inference endpoints, caching layers, cost management systems, and fallback logic that make AI capabilities reliable components in your product architecture.

AI Technology Stack

LLM Providers: OpenAI GPT-4/o, Anthropic Claude, Google Gemini, Meta Llama, Mistral, Cohere

Frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, Haystack, Semantic Kernel

Vector Databases: Pinecone, Weaviate, Qdrant, Chroma, pgvector, Milvus

Model Training: PyTorch, TensorFlow, JAX, Hugging Face Transformers, PEFT, LoRA

Computer Vision: OpenCV, Detectron2, YOLOv8/v11, SAM, CLIP, Torchvision

Serving: FastAPI, vLLM, TGI (Text Generation Inference), Triton Inference Server, ONNX Runtime

MLOps: MLflow, Weights & Biases, DVC, BentoML, Modal, Replicate

Cloud: AWS SageMaker, GCP Vertex AI, Azure ML, Lambda (GPU), Bedrock, Together AI

Client Success Story: Private RAG System for a Major Financial Services Enterprise

A financial services enterprise with thousands of employees needed its workforce to query complex regulatory frameworks, internal compliance policies, and contract libraries in natural language — without sending sensitive data to external API providers. Existing keyword search was returning too many results to be actionable. Our AI engineers built a private retrieval-augmented generation system using a self-hosted LLM, a Weaviate vector database, and a hybrid retrieval architecture combining semantic similarity search with BM25 keyword matching for precision on technical financial terminology. The system scored 89% accuracy on an internal benchmark of 500 representative compliance queries — a level the legal and compliance team validated as sufficient for deployment. Query resolution time dropped from an average of 45 minutes to 90 seconds.

Client Success Story: Multi-Agent Customer Operations Platform for a SaaS Company

A fast-growing SaaS company was processing 50,000 support tickets monthly with a team that couldn’t scale proportionally with ticket volume without harming unit economics. Standard chatbot approaches had been tried and abandoned — they resolved too few tickets and frustrated customers on edge cases. Our AI engineers built a multi-agent orchestration system using LangGraph — with specialized agents for ticket triage, knowledge base retrieval, direct resolution, and human escalation routing. Each agent operated with its own tools, memory, and fallback logic. The system autonomously resolved 72% of tickets from day one. The support team remained flat headcount while ticket volume tripled over the following year.

Why Companies Choose Our AI Developers

• Production-grade, not demo-grade: Our AI developers have shipped AI into production, managed the reliability, cost, and latency trade-offs that production AI demands, and operated systems at scale — they don’t just build demos
• Full-spectrum AI: From LLM prompt engineering to computer vision to fine-tuning to MLOps — our AI engineers aren’t specialists in one narrow technique, they understand the full AI engineering stack
• Cost management expertise: AI inference at scale is expensive. Our engineers build cost-aware systems with caching, model routing, batch inference, and intelligent fallbacks that control LLM API costs without compromising quality
• Evaluation-driven development: They don’t ship without evals. Our AI developers build evaluation frameworks, track regression metrics, and make AI system improvements with data — not intuition
• 50% cost savings: Fully-burdened rates including salary, benefits, taxes, and HR

Engagement Models

• Individual AI Engineer — A senior AI developer embedded in your existing product or engineering team. Ideal for adding an LLM application specialist, a RAG system architect, or an AI evaluation framework engineer to a team that’s ready to move from AI experiments to production AI features.
• AI Development Pods (AI engineer + ML engineer + MLOps) — A cross-functional AI squad covering application development, model selection and fine-tuning, and production infrastructure. Common for companies launching their first production AI product or overhauling an existing AI feature that isn’t performing reliably.
• Full AI Teams (5–20+ engineers) — Complete AI engineering organizations for companies where AI is the product — covering LLM application engineering, model fine-tuning, RAG infrastructure, computer vision pipelines, and the evaluation and monitoring systems that keep everything reliable.
• Contract-to-Hire AI Engineers — Evaluate an AI engineer’s production judgment on your actual AI systems before making a permanent commitment. The quality of an engineer’s evaluation framework and prompt engineering discipline become visible quickly.

How To Vet AI Developers

Our AI vetting screens specifically for production AI discipline — not notebook-level ML skill. The four-stage process:

1. Technical screening — LLM application architecture (RAG pipeline design, vector database selection, chunking and embedding strategies, context window management), agent framework design (LangChain, LlamaIndex, custom agent loops), prompt engineering and output reliability, and AI evaluation methodology. Over 90% of applicants do not pass this stage.
2. Take-home exercise — Build a production-quality RAG system: document ingestion pipeline with chunking strategy, embedding model selection with rationale, vector store integration (Pinecone, pgvector, or Weaviate), retrieval with metadata filtering, and an evaluation framework using RAGAS or equivalent. Evaluated on retrieval quality, context precision, and the evaluation methodology itself.
3. Live technical interview — RAG debugging (diagnosing retrieval failures, hallucination patterns, context poisoning), agent loop architecture for a multi-step AI workflow, cost-performance trade-off analysis for model selection, and discussion of when fine-tuning is preferable to retrieval-augmented approaches.
4. Communication and stakeholder screening — AI engineers explain probabilistic system behavior to executives and product teams who expect deterministic software. We screen for the ability to set appropriate expectations, communicate failure modes, and quantify AI system reliability in business terms.

What to Look for When Hiring AI Developers

Strong AI developers treat AI system reliability as an engineering problem — not a prompting problem. They build evaluation frameworks before they ship features, and they can quantify their system’s behavior under adversarial inputs.

What strong candidates demonstrate:

• They design RAG pipelines with retrieval quality in mind from the start: they choose chunking strategies based on document structure, evaluate embedding models against their specific domain, and measure retrieval precision and recall before measuring end-to-end answer quality
• They have a disciplined approach to prompt engineering: they version prompts, test them against a fixed evaluation set, and measure regression when prompts change — they don’t iterate prompts in production based on user complaints
• They understand the real cost of LLM calls at scale: they implement caching strategies (semantic cache, exact-match cache), choose model tiers deliberately (GPT-4o vs. GPT-4o-mini vs. Claude Haiku based on task requirements), and build cost dashboards that surface per-feature LLM spend
• They build observable AI systems: every LLM call is logged with inputs, outputs, latency, token counts, and a retrieval trace — so when the system fails, they know exactly what happened

Red flags to watch for:

• Building AI features without an evaluation framework — a sign they’ll have no systematic way to know if a prompt change improved or regressed quality
• Describing AI system reliability purely in terms of prompting — candidates who believe reliability is a prompt engineering problem haven’t operated AI systems in production at scale
• Using maximum-tier models (GPT-4o, Claude Opus) for every AI call without cost analysis — a sign they haven’t thought about the economics of AI at production scale
• No structured logging on LLM calls — teams flying blind on AI system behavior can’t improve it systematically

Interview questions that reveal real depth:

• “Walk me through how you’d evaluate the quality of a RAG system. What metrics would you use, how would you build the evaluation dataset, and how would you prevent prompt regressions from reaching production?”
• “A RAG system is hallucinating answers to questions that appear to be covered in the document corpus. Walk me through your debugging process — what are the likely failure modes and how would you diagnose each?”
• “You’re designing a multi-step AI agent that calls external tools. How do you handle tool call failures, partial completions, and loops? What observability would you build into the agent loop?”

Related Services

• Hire ML Engineers — The model specialists behind your AI applications. Explore our machine learning engineering practice.
• Hire MLOps Engineers — The infrastructure that keeps AI systems running reliably in production.
• AI Engineers & ML Engineers — Explore our full AI and ML engineering practice across application, model, and infrastructure layers.
• Data Scientists & Data Engineers — AI needs data. Our data engineers build the pipelines that feed your AI systems.

Want to Hire Remote AI Developers?

We specialize in sourcing, vetting, and placing senior remote AI engineers — from individual LLM application developers who build evaluation frameworks before they ship, to complete AI engineering organizations building RAG systems, multi-agent platforms, fine-tuned domain models, and production inference infrastructure. We make it fast, affordable, and low-risk.

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Ready to hire AI developers who build RAG pipelines with real retrieval quality, agent loops with observable failure modes, and LLM cost management that makes AI economics work? Contact us today and we’ll introduce you to senior AI engineers within 48 hours.