Data AI Agents
A curated collection of 16 data ai agents for building with AI coding agents.
Agent Tool Integration Engineer
Use this agent to wire tools and function-calling into an agent loop reliably — clean tool schemas, errors fed back as observations, retries with limits, idempotency, and parallel calls. Examples — "connect our APIs as agent tools", "our agent calls tools wrong / ignores tool errors", "add function-calling with proper error recovery to our agent".
Data Engineer
Use this agent to build and maintain data pipelines — ingestion, ELT/ETL, warehouse modeling, orchestration, and data-quality tests. Examples — building an idempotent ingestion job, modeling a fact/dimension table in dbt, writing a safe backfill for a changed schema.
Data Scientist
Use this agent for data analysis — exploration, statistics, SQL, and clear findings. Examples — analyzing a dataset, writing an analytical SQL query, summarizing experiment results.
Finetuning Engineer
Use this agent to fine-tune an open-weight model end to end — confirming fine-tuning is the right tool, preparing the dataset, choosing the method (LoRA/QLoRA vs. full), running training, and proving the result beats the prompted baseline on a held-out eval set. Examples — "fine-tune a small model to match our support tone and answer format", "we have 800 labeled examples — LoRA-tune and show it beats prompting", "our fine-tune overfits and forgot general ability — fix the data and run".
LLM Cost Optimizer
Use this agent to cut the cost and latency of an application's LLM API usage without losing quality — audit where the tokens and dollars go, then apply caching, model right-sizing, prompt trimming, batching, and budgets, proven against an eval bar. Examples — "our OpenAI bill tripled, find where the spend is and cut it", "this endpoint's p95 is 8s, bring it down", "right-size models per task and add prompt caching to our chat feature".
LLM Evaluation Engineer
Use this agent to make an LLM feature's quality measurable — building the dataset, choosing metrics, setting a baseline, and turning evals into a CI gate so prompt and model changes are scored, not guessed. Examples — "we changed the prompt and don't know if it's better, set up evals", "add a regression gate for our extraction feature", "our RAG quality is drifting, build an eval suite".
LLM Inference Engineer
Use this agent to serve and optimize self-hosted LLM inference — sizing GPUs, configuring a serving engine like vLLM (continuous batching, PagedAttention, tensor parallelism), applying quantization, and tuning throughput and tail latency against a cost and p95 budget. Examples — "serve Llama-3-70B at p95 under 2s on our GPUs", "our self-hosted model is slow and the GPUs sit half-idle — raise throughput", "quantize this model to fit one GPU without wrecking quality".
LLM Integration Engineer
Use this agent to add an LLM feature to an application and make it production-grade — typed/structured output, streaming, provider fallback and retries, caching, and cost/latency controls. Examples — "add an AI summary endpoint to our app", "our LLM calls return unparseable JSON and break, make them reliable", "add streaming and a fallback provider to our chat feature".
LLM Observability Engineer
Use this agent to make a production LLM app observable — tracing every step, scoring live traffic with online evals, and monitoring quality, cost, and latency — so you can debug agent runs and catch regressions in production. Examples — "add tracing to our RAG/agent so we can debug bad answers", "set up online evals and cost/latency dashboards", "production quality is slipping and we're flying blind".
ML Engineer
Use this agent for production ML — pipelines, training, serving, evaluation, and MLOps. Examples — building a training pipeline, deploying a model, setting up evaluation.
Postgres Migration Engineer
Use this agent to plan and execute a zero-downtime Postgres schema migration — decomposing a breaking change into expand-contract steps, writing batched backfills, building indexes CONCURRENTLY, validating constraints online, and keeping every step reversible with the project's migration tooling. Examples — "add a NOT NULL column to a 200M-row table without downtime", "rename a column safely across a rolling deploy", "split this risky migration into reversible expand/contract steps".
Prompt Engineer
Use this agent to design and iterate the prompts behind an LLM-powered product feature — instructions, few-shot examples, tool schemas, and the evals that prove a change actually helped. Examples — "this classification prompt is flaky, make it reliable", "design the system prompt and function schema for our support agent", "our extraction prompt regressed after I tweaked it, set up evals so this stops happening".
Rag Pipeline Engineer
Use this agent to design, build, and harden a production retrieval-augmented generation (RAG) pipeline end to end — ingestion, chunking, embeddings, indexing, retrieval, reranking, and grounded generation — with evals that prove each stage works. Examples — "stand up RAG over our docs", "our RAG hallucinates and misses obvious answers, fix the pipeline", "take our prototype RAG to production with evals and citations".
Retrieval Engineer
Use this agent to raise the retrieval quality of a search or RAG system — recall and precision, hybrid (dense + sparse) search, reranking, query transformation, and metadata filtering — measured against a labeled eval set. Examples — "our RAG retrieves irrelevant chunks, fix recall", "add hybrid search and reranking and prove it helps", "queries with acronyms/IDs return nothing, fix it".
Vector Search Engineer
Use this agent to design, build, and tune the vector-database layer of a search or RAG system — schema and index design (HNSW/IVF + quantization), metadata/payload filtering, hybrid (dense + sparse) search, and ingestion/upsert pipelines — sized to a real latency, recall, and cost budget. Examples — "set up pgvector for our docs with HNSW and filtered search", "our Qdrant queries are slow and recall dropped after quantization", "add metadata filtering so search only returns the current tenant's documents".
Voice Agent Engineer
Use this agent to build or fix a real-time voice agent — the streaming STT → LLM → TTS pipeline, conversational (mouth-to-ear) latency, turn-taking, barge-in/interruptions, and per-stage provider selection. Examples — "our voice bot feels laggy and talks over people, fix the turn-taking and latency", "build a phone agent that transcribes, answers with our LLM, and speaks back", "get our voice agent's response time under a second".