Hire AI Engineers Who Build for Production Build for Production
Everyone has a ChatGPT wrapper. You need something that actually works at scale. Our AI/ML engineers build real systems — fine-tuned models, RAG pipelines that return accurate results, and ML infrastructure that handles millions of predictions without falling over. We focus on production-grade AI, not impressive demos that break the moment real users show up. Most teams start within 48 hours.
Why Hire Dedicated AI/ML Engineers?
AI talent is expensive and hard to evaluate. A bad hire can waste 6 months building something that never makes it past a Jupyter notebook. Here is why companies hire through us instead of going it alone.
- Skip the 6-month hiring cycle — finding a good ML engineer on your own takes 4-6 months and costs $15-30K in recruiter fees. Our engineers are pre-vetted on real ML tasks (not just LeetCode problems) and available within days, not months.
- Production experience, not just research — most ML candidates can train a model in a notebook. Far fewer can deploy it behind an API, monitor for data drift, set up automated retraining pipelines, and keep latency under 200ms. Our engineers have done this across 50+ production deployments.
- Stay current without burning budget — the AI field moves fast. GPT-4, Claude, Llama, Mistral, new embedding models every month. Our engineers work across multiple client projects and stay current on what actually works vs what is just hype. You get that knowledge without paying for their continuous learning.
- Right-size your AI investment — not every problem needs a custom-trained model. Sometimes a well-crafted prompt chain with GPT-4 solves the problem at 1/10th the cost. Our engineers will tell you honestly when a simpler approach works better, saving you months and tens of thousands of dollars.
What Our AI/ML Engineers Build
Our engineers work across the full ML lifecycle — from data preparation to model serving. Here are the specific skills they bring.
LLM Fine-Tuning and Prompt Engineering
Fine-tuning GPT, Llama, and Mistral models on your domain data using LoRA and QLoRA. Building prompt chains with LangChain or LlamaIndex. We have fine-tuned models for legal document analysis, medical triage, and customer support — with measurable accuracy improvements of 15-40% over base models.
RAG Pipelines and Vector Search
Retrieval-augmented generation using Pinecone, Weaviate, Chroma, or pgvector. We build chunking strategies, embedding pipelines, and reranking systems that return relevant results — not just similar-sounding text. Average accuracy improvement over naive RAG: 25-35%.
Traditional ML and Predictive Models
Not everything needs a large language model. We build fraud detection, demand forecasting, recommendation engines, and churn prediction using XGBoost, LightGBM, and scikit-learn. These models are often faster, cheaper, and more accurate for structured data problems.
MLOps and Model Deployment
Model serving with TorchServe, Triton, or BentoML. CI/CD for ML pipelines using MLflow or Weights & Biases. Automated retraining triggers based on data drift detection. We get models out of notebooks and into production with proper monitoring, versioning, and rollback capabilities.
Computer Vision Systems
Object detection, image classification, OCR, and video analysis using PyTorch and YOLO. We have built quality inspection systems for manufacturing, document processing for insurance, and medical imaging analysis — all running in production with sub-second inference times.
Data Pipeline Engineering
ML models are only as good as their data. Our engineers build data labeling workflows, feature stores, ETL pipelines, and data quality monitoring. We work with tools like Apache Airflow, dbt, Great Expectations, and Labelbox to keep your data clean and your models accurate.
Who Hires AI/ML Engineers Through Us
AI adoption looks different for a 10-person startup vs a Fortune 500. Here are the most common scenarios we support.
AI-First Startups Building Their Core Product
You have a working prototype and seed funding. Now you need to turn that Jupyter notebook into a real product. Our engineers handle model optimization (reducing inference costs by 50-70%), API development, and the MLOps infrastructure that lets you iterate quickly. We have helped 15+ AI startups go from prototype to production.
Enterprise Companies Adding AI to Existing Products
You have millions of users and a working product. Adding AI features — smart search, document understanding, automated categorization — needs to happen without breaking what already works. Our engineers integrate with your existing tech stack and roll out AI features gradually with feature flags and A/B testing.
Healthcare Organizations Deploying Clinical AI
Medical AI has unique requirements: FDA regulatory awareness, HIPAA-compliant model training, explainability for clinical decisions, and zero tolerance for hallucination in patient-facing outputs. Our engineers have built clinical decision support tools, medical image analysis systems, and patient triage chatbots with appropriate safeguards.
Fintech Companies Fighting Fraud
Real-time fraud detection needs models that make decisions in under 50ms, handle millions of transactions daily, and adapt to new attack patterns without manual retraining. Our engineers build ensemble models that catch 95%+ of fraud while keeping false positive rates below 1%, using a mix of rule-based systems and ML.
From AI Idea to Production Model
Hiring an AI engineer should not be harder than building the model itself. Here is our process.
Scope Your AI Problem
A 30-45 minute call where we dig into the specifics. What data do you have? What problem are you solving? What does success look like? We will tell you honestly if AI is the right approach or if a simpler solution works better. About 20% of the time, we recommend a non-ML approach and save our clients significant time and money.
Match You With Specialists (48 Hours)
Based on your problem, we present 2-3 engineers with relevant experience. Building a RAG system? You get engineers who have built RAG before. Need computer vision? You get CV specialists. Each profile includes past ML projects, model accuracy metrics they have achieved, and their preferred tech stack.
Technical Deep Dive
Your selected engineer does a technical assessment of your data, existing infrastructure, and requirements. They will build a small proof of concept if needed — usually within 3-5 days — to validate the approach before you commit to a full engagement. This is included in the trial period.
Two-Week Trial With a Working Deliverable
Not just planning documents. By the end of week two, you have a working model or pipeline — maybe a baseline RAG system returning relevant results, a trained classification model with measured accuracy, or a deployed API endpoint you can test. If the results are not there, you do not pay.
Scale the Engagement
Once the approach is validated, we scale up — adding more engineers, expanding scope, or transitioning to ongoing model maintenance. We create a roadmap with clear milestones so you always know what you are paying for and when you will see results.
Ready to get started? Let's discuss your project.
AI/ML Engineer Pricing
AI engineers cost more than general developers — that is the reality of the market. But our rates are 40-60% lower than US-based hires with the same skill level. All engineers have 3+ years of production ML experience.
Individual AI/ML Engineer
One dedicated engineer for your AI project. Best for focused work like building a single model, setting up a RAG pipeline, or integrating an LLM into your product.
Custom pricing based on your requirements
- Senior ML engineer (3-7 years experience)
- 40 hours/week on your project
- Direct access via Slack and video
- Weekly experiment reports with metrics
- Model documentation and handoff included
- Starting at $5,500/month
AI Pod (2-3 Engineers)
A small team covering different parts of the ML lifecycle — data engineering, model development, and MLOps. Ideal for companies building their first AI product or adding multiple AI features.
Custom pricing based on your requirements
- 2-3 engineers across ML and data engineering
- End-to-end coverage from data to deployment
- Shared model experiment tracking
- Bi-weekly demos with stakeholders
- Full MLOps pipeline setup included
- Starting at $13,000/month
Full AI Team
A complete AI team with a lead ML architect, engineers, and a data engineer. For companies making AI a core part of their product strategy and needing sustained development capacity.
Custom pricing based on your requirements
- 4-5 specialists including a lead architect
- Full ownership of your AI/ML stack
- Continuous model monitoring and retraining
- Monthly executive AI strategy sessions
- Research and benchmarking of new approaches
- Starting at $25,000/month
Hire AI/ML Engineers Questions Answered
Quick answers to the questions we hear most often.
PyTorch is our primary framework — about 70% of our work uses it. We also work with TensorFlow, JAX, Hugging Face Transformers, LangChain, LlamaIndex, and scikit-learn. For LLM work, our engineers are experienced with the OpenAI API, Anthropic Claude API, and open-source models like Llama and Mistral. The specific framework depends on your use case — we will recommend what makes the most sense rather than defaulting to whatever is trendiest.
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