Hire Offshore AI Agent Developers

Agent architecture

• • Design multi-step reasoning flows, tool-calling chains, and planner/executor patterns
• • Choose between single-agent, multi-agent, and graph-based orchestration based on the task
• • Document state management, memory, and handoff contracts between agent steps

RAG & knowledge base engineering

• • Build retrieval pipelines on Pinecone, Weaviate, or pgvector with hybrid search
• • Design chunking, embedding, and re-ranking strategies tuned to your content
• • Keep knowledge bases fresh with incremental indexing and deletion hooks

LLM integration

• • Integrate OpenAI, Anthropic, and Google APIs with streaming, function calling, and retries
• • Run open-source models locally or on Modal, Replicate, or Together for cost-sensitive workloads
• • Route queries across models by task complexity, latency, and price per token

Evaluation & guardrails

• • Ship an eval harness with golden datasets, LLM-as-judge scoring, and regression tracking
• • Add output validation with Pydantic schemas and structured output modes
• • Detect hallucinations with grounding checks against retrieved context

Deployment & scaling

• • Deploy agents on Vercel, Railway, AWS Lambda, or Modal with Docker and health checks
• • Stream responses to the client with SSE or WebSockets for chat interfaces
• • Handle rate limits, retries, and circuit breakers across upstream LLM providers

What AI frameworks and models do they specialize in?

Our shortlists cover the LangChain and LangGraph ecosystem, LlamaIndex, the Vercel AI SDK, and direct use of the OpenAI, Anthropic, and Google SDKs without a framework. On models, every candidate has shipped production work against GPT-4o, Claude Sonnet, and Gemini, and most have experience with open-source models like Llama 3, Mistral, and Qwen running on Modal, Together, or Replicate. If you already have a preferred stack we match candidates who have shipped on that exact stack rather than sending generalists.

How do you handle hallucinations and output quality?

Every production agent ships with an eval harness before it hits real users. Your developer builds a golden dataset of 50–200 representative inputs, scores outputs with LLM-as-judge and exact-match metrics, and tracks regressions across prompt and model changes. For grounded answers, outputs are checked against the retrieved context and flagged when the agent cites something not in the sources. Structured outputs use Pydantic schemas with validation retries, and critical flows get human-in-the-loop review queues before actions fire.

Can they build voice agents (Vapi, Retell) not just text?

Yes, about 30% of our AI agent developers have shipped production voice agents on Vapi, Retell, LiveKit, or custom Twilio pipelines. Voice work adds real-time constraints (latency budgets under 800ms, interruption handling, partial transcripts) and usually involves Deepgram or Whisper for STT and ElevenLabs or OpenAI for TTS. If voice is your primary use case we flag it at the kickoff call so we only shortlist candidates with voice agent deployments on their resume.

How do you manage LLM API costs at scale?

Your developer tags every LLM call with workflow name, user ID, and model, then logs usage to PostHog or a Postgres table with a Grafana dashboard on top. Cost optimization passes include prompt caching on Anthropic, routing cheap queries to smaller models, batching embeddings, truncating context windows, and caching frequent retrievals. Most clients see 40–60% cost reduction after the first optimization pass without any quality loss, and spend stays predictable under a monthly budget with alerts at 50%, 80%, and 95%.

Do they have experience shipping production agents, not just demos?

Yes. Every candidate in our AI agent shortlist has at least one production agent serving real users — not a LangChain tutorial or a weekend hackathon project. In the technical interview they walk through a specific agent they shipped, why it failed the first few iterations, how they caught and fixed the failures, and what they would build differently today. We reject candidates whose only experience is notebook demos or prompt engineering without deployment experience.