Hire AI Engineers
Hire AI Engineers Who Deploy, Not Just Prototype
Hire senior AI engineers who build production-ready ML systems, LLM applications, and intelligent automation. Our engineers integrate with your team or work within managed Pods—building models, pipelines, and shipping AI that works in the real world.
Why Salt
Why Hire AI Engineers from Salt
When you hire AI engineers through Salt, you get more than skills on a resume. Our engineers are vetted for real-world expertise—building ML systems that work in production, not just in notebooks.
Pre-Vetted ML Specialists
Only 3% of applicants pass our screening. ML system design, coding exercises, model evaluation knowledge, and production deployment experience.
Production AI Expertise
Not just Jupyter notebooks. Our engineers deploy ML systems to production with proper MLOps, monitoring, and governance.
Fast Onboarding
Start interviewing within days, not weeks. Our pre-vetted pool means you skip months of recruiting for specialized AI talent.
Risk-Free Trial
2-week trial with every hire. If it's not working, we replace at no extra cost. Your satisfaction guaranteed.
LLM & GenAI Ready
Our engineers work with the latest: GPT-4, Claude, LangChain, RAG systems. They know how to build real applications, not just demos.
Outcome-Oriented
Not just model builders—engineers who own the full ML lifecycle from problem definition to production deployment and monitoring.
Technical Expertise
AI & ML Skills Our Engineers Bring
From classical ML to cutting-edge LLMs, our engineers have deep experience across the AI/ML stack.
Machine Learning
- Supervised & Unsupervised Learning
- Deep Learning (CNN, RNN, Transformers)
- Natural Language Processing
- Computer Vision
- Reinforcement Learning
Generative AI & LLMs
- OpenAI GPT / ChatGPT APIs
- LangChain & LlamaIndex
- Prompt Engineering
- RAG (Retrieval Augmented Generation)
- Fine-tuning & PEFT
ML Frameworks & Tools
- PyTorch & TensorFlow
- scikit-learn
- Hugging Face Transformers
- Keras
- JAX / Flax
Data Engineering
- Feature Engineering
- Data Pipelines (Airflow, Prefect)
- Vector Databases (Pinecone, Weaviate)
- Apache Spark / Databricks
- Data Versioning (DVC)
MLOps & Infrastructure
- MLflow / Weights & Biases
- Kubeflow / SageMaker Pipelines
- Model Serving (TorchServe, TFServing)
- Docker & Kubernetes
- Model Monitoring & Drift Detection
Cloud AI Platforms
- AWS SageMaker
- Google Vertex AI
- Azure ML
- OpenAI / Azure OpenAI Service
- Hugging Face Inference Endpoints
Capabilities
What Our AI Engineers Deliver
Beyond building models—our engineers own the full ML lifecycle and ship AI that works in production.
LLM Applications & AI Copilots
Build intelligent chatbots, AI assistants, and copilots using GPT-4, Claude, or open-source models. RAG systems, agents, and conversational AI.
Custom ML Models
Design, train, and deploy custom machine learning models for classification, prediction, recommendation, and anomaly detection use cases.
End-to-End ML Pipelines
Build production ML pipelines from data ingestion to model serving. Automated retraining, A/B testing, and continuous integration for ML.
Computer Vision & NLP
Image classification, object detection, OCR, text classification, entity extraction, sentiment analysis, and document understanding systems.
Responsible AI & Governance
Implement model explainability, bias detection, and AI governance frameworks. Ensure your AI systems are fair, transparent, and compliant.
Team Integration & Mentorship
Participate fully in your development process including code reviews, architecture discussions, and knowledge transfer. Elevate your team's AI capabilities.
How to Hire AI Engineers
Hire AI Engineers in Weeks, Not Months
Our streamlined hiring process gets pre-vetted AI engineers on your team fast. Skip the lengthy recruitment cycles.
Share Your Requirements
Tell us about your AI/ML project, tech stack (PyTorch, TensorFlow, LLMs), and the experience level you need. We'll help you define the right profile.
Get Matched Profiles
We present 2-3 pre-vetted AI engineers who match your requirements. Review their experience, ML projects, and our assessment notes.
Interview & Select
Interview candidates directly with technical questions relevant to your ML stack. Discuss system design, past projects, and approach. You decide.
Risk-Free Trial
Start with a 2-week trial. The engineer joins your team, understands your data, and delivers real work. If not a fit, we replace at no cost.
Scale as Needed
Add more AI engineers or build a full AI/ML Pod with data engineers, MLOps, and a tech lead. We scale with your needs.
Engagement Options
Individual AI Engineers or AI/ML Pods
Choose the model that fits your needs. Start with one engineer, scale to a full AI team.
Dedicated AI Engineer
A skilled AI/ML engineer who works as part of your team. You provide direction and domain context; they build and deploy models.
- Full-time dedication to your AI project
- Integrates with your data and ML stack
- Participates in your standups and reviews
- Scale up or down as needed
AI/ML Pod
A cross-functional team including AI engineers, data engineers, MLOps, and a tech lead. We handle delivery; you focus on what to build.
- Full accountability for ML features
- Built-in data engineering and MLOps
- SPARK™ framework for quality gates
- Tech lead for architecture decisions
Not sure which is right? Read our comparison guide
Ready to Hire AI Engineers?
Tell us about your AI/ML project and requirements. We'll present pre-vetted candidates within days, and you can start with a risk-free trial.
FAQs
Hire AI Engineers: Common Questions
Answers to frequently asked questions about hiring AI engineers through Salt.
How quickly can I hire AI engineers through Salt?
You can hire AI engineers within 1-2 weeks. We present pre-vetted candidates within 2-3 days, you interview and select, and engineers can start with a 2-week trial. No lengthy recruitment cycles or months of searching for specialized AI talent.
What skills do your AI engineers have?
Our AI engineers are proficient in Python, PyTorch, TensorFlow, scikit-learn, LangChain, OpenAI APIs, Hugging Face, MLOps tools (MLflow, Kubeflow, Weights & Biases), cloud AI services (AWS SageMaker, GCP Vertex AI, Azure ML), and vector databases (Pinecone, Weaviate). Many have experience deploying production ML systems at scale.
How do you vet AI engineers before I hire them?
Our vetting includes ML system design discussions, live coding for data pipelines and model training, evaluation of MLOps practices, and assessment of production deployment experience. We test understanding of model evaluation metrics, feature engineering, experiment tracking, and responsible AI principles. Only about 3% pass.
Can I hire AI engineers for my existing team?
Yes, absolutely. When you hire AI engineers through Salt, they integrate seamlessly with your existing team, processes, and tools. They participate in your standups, code reviews, and planning sessions. We specifically match engineers who fit your tech stack and team culture.
Can I hire engineers for LLM and generative AI projects?
Yes. We have engineers experienced in building LLM applications using OpenAI GPT-4, Anthropic Claude, and open-source models. They're proficient in LangChain, RAG architectures, prompt engineering, fine-tuning, and deploying AI copilots and chatbots to production.
What experience level are the AI engineers I can hire?
Most AI engineers you can hire through Salt have 4-8+ years of experience building production ML systems. We categorize engineers as Mid-level (3-5 years), Senior (5-8 years), and Staff/Principal (8+ years) based on demonstrated skills, not just tenure.
Can I hire AI engineers who also handle data engineering?
Yes. Many of our AI engineers are comfortable with data engineering tasks including building data pipelines, feature stores, and data quality frameworks. If you need dedicated data engineering capacity, we recommend hiring a full-stack ML engineer or a Pod that includes data engineers.
What if I need to hire more AI engineers later?
Scaling is built into our model. Start by hiring one AI engineer, add more as needed, or transition to a full AI/ML Pod with additional skills (data engineering, MLOps, frontend). There's no penalty or friction for scaling up or down.
What time zones do your AI engineers work in?
Our engineers are based in India but maintain 4-6 hours of overlap with US time zones. Many work flexible schedules to align with your team's core hours. We ensure there's enough real-time collaboration for effective teamwork.
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