AI & Machine Learning/Expert/1 day ago
Voltify wants to reduce first-line support load with an LLM assistant that answers questions from our documentation and past resolved tickets. It must escalate gracefully to a human when confidence is low.
The work: build the retrieval pipeline over our docs (Markdown) and ticket history (Zendesk export), design the prompt and guardrails, and expose it behind a small API our web team can call. Evaluation matters: we want a test set and accuracy reporting, not vibes.
You will work directly with our support lead. Hourly, roughly 15 to 20 hours per week.
PythonLLM IntegrationMachine Learning
$60.00 - $100.00/hr2 bids
AI & Machine Learning/Expert/2 days ago
We run Deskhive, a helpdesk platform used by around 400 small SaaS companies, and we want to ship an AI assistant that drafts replies for support agents using each customer's own knowledge base. The retrieval side is the hard part: docs live in Postgres, they update constantly, and answers must cite their sources so agents can verify before sending.
Scope: design the chunking and embedding pipeline, set up pgvector on our existing PostgreSQL cluster, build the retrieval + generation service in Python (FastAPI), and expose it through a clean internal API our Node backend can call. Deliverables include an evaluation harness with at least 50 golden Q&A pairs per tenant so we can measure answer quality before rollout.
You would work directly with our CTO and one backend engineer. We do a 30-minute sync twice a week and everything else async in Linear and Slack. Please include a short note about a retrieval system you have actually shipped, not just prototyped.
LLM IntegrationPythonPostgreSQL
$3,500.00 - $5,200.00 fixed price0 bids
AI & Machine Learning/Expert/6 days ago
Sightline Robotics does automated defect detection for packaging lines. Our two vision models currently run on a single beefy EC2 box that an intern set up, and it falls over whenever a second factory comes online. We need a proper serving setup: containerized inference with Docker, autoscaling on EKS, GPU node groups, blue-green model rollouts, and latency monitoring with alerts.
You would own the infrastructure end to end - Terraform or CDK (your call, argue for it), CI that builds and pushes model images, a canary process for new model versions, and runbooks our two ML engineers can actually follow at 2am. Target: p95 inference under 200ms at 40 requests/second per site, and a new model version deployable in under 15 minutes without dropping requests.
We estimate 15-20 hours a week alongside our team. Timezone overlap with US Central for at least 3 hours daily is required because rollouts happen during factory maintenance windows.
AWSKubernetesDockerMachine Learning
$75.00 - $115.00/hr0 bids
AI & Machine Learning/Intermediate/9 days ago
Our marketplace aggregates home improvement products from about 60 suppliers, and every supplier categorizes things differently - one vendor's "fixtures" is another's "bath hardware". We have 180k SKUs, roughly 40k of them hand-labeled into our 900-node category tree by our ops team over the past year, and we want a model that classifies the rest and handles new inbound feeds.
The work: explore the labeled data, pick an approach (we suspect fine-tuning a small transformer on title + description + supplier attributes will beat classic TF-IDF, but convince us with numbers), train it, and hand over a documented Python inference service plus a confidence-thresholded review queue design so low-certainty items route to humans. We measure success as >=92% top-1 accuracy on a held-out set our team curates.
Expect an async-first engagement. We will give you a sanitized data export on day one and access to a staging database. Weekly written progress updates matter more to us than meetings.
Machine LearningPythonData Analysis
$2,400.00 - $4,000.00 fixed price0 bids
AI & Machine Learning/Intermediate/15 days ago
Every month roughly 7% of our meal-kit subscribers cancel, and today we only find out when they hit the cancel button. We want a weekly churn-risk score per subscriber so our retention team can intervene earlier with pause offers or box swaps. All behavioral data (orders, skips, support contacts, delivery issues) sits in a reasonably clean PostgreSQL warehouse going back three years.
Deliverables are concrete: an exploratory analysis writeup identifying the strongest churn signals, a trained model (we lean gradient boosting for interpretability, open to arguments), a scoring script that runs weekly and writes scores back to a table, and a one-page explanation of the top risk factors our non-technical retention lead can act on. No production ML infrastructure needed - a scheduled Python job is fine for now.
We are a 30-person company in Denver, mostly async, Slack for questions. Our data analyst can pair with you on warehouse quirks during your first week.
Machine LearningPythonPostgreSQL
$2,000.00 - $3,200.00 fixed price0 bids
AI & Machine Learning/Intermediate/22 days ago
Three years of NPS follow-up comments, in-app feedback, and churn survey answers are sitting in a spreadsheet nobody reads - about 90k free-text entries in English and Spanish. We build field service software and we know the answers to our roadmap debates are buried in there.
What we need: an embedding-and-clustering pipeline that groups the responses into a stable theme taxonomy (we expect 30-60 themes), LLM-generated labels and representative quotes per theme, and a repeatable Python script our team can rerun quarterly as new feedback arrives. The final readout is a written report ranking themes by volume and by correlation with churned accounts.
Short, focused project. We will judge proposals on whether you describe how you would validate cluster quality - if the plan is just "run k-means and eyeball it", it is not for us.
Machine LearningLLM IntegrationPython
$1,300.00 - $2,400.00 fixed price0 bids