AI Engineers & ML Engineers

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Build Production AI Systems That Generate $50M+ Revenue With Senior AI Engineers & ML Engineers

Ready to transform your business with AI but struggling to find engineers who can actually deliver? We match you with AI and ML engineers who’ve already built the production systems, deployed the models, and created the AI-powered products that drove 8-figure business outcomes.

The AI and ML engineers we staff have contributed to breakthrough AI implementations at Fortune 500 companies and unicorn startups that have leveraged AI to achieve billion-dollar valuations and transform entire industries including e-commerce, gaming, healthcare, finance, and automotive.

AI Engineer vs ML Engineer vs Prompt Engineer: Which Do You Need?

Understanding the distinctions between these roles is crucial for building the right AI team:

AI Engineer

Focus: Building and deploying AI systems, integrating models into production applications, and creating AI-powered products

  • Develops end-to-end AI applications and user-facing AI features
  • Integrates multiple AI models into cohesive systems
  • Builds AI infrastructure, APIs, and deployment pipelines
  • Creates AI-powered products and customer experiences
  • Best for: AI product development, system integration, production deployment

ML Engineer

Focus: Training, optimizing, and deploying machine learning models with emphasis on MLOps, model performance, and infrastructure

  • Designs and trains custom machine learning models
  • Optimizes model performance, accuracy, and efficiency
  • Builds MLOps pipelines for model lifecycle management
  • Handles data preprocessing, feature engineering, and model monitoring
  • Best for: Custom model development, MLOps, performance optimization

Prompt Engineer

Focus: Crafting effective prompts, optimizing LLM outputs, and designing conversational AI experiences

  • Designs prompt strategies for maximum LLM effectiveness
  • Creates conversational flows and AI interaction patterns
  • Optimizes prompt chains and multi-step reasoning
  • Builds RAG systems and knowledge retrieval pipelines
  • Best for: LLM optimization, conversational AI, prompt-based applications

Most successful AI projects require a combination of these roles working together.

Cutting-Edge AI/ML Use Cases Our Engineers Build

1. Sora-Style Text → Video Generation

Technical Implementation:

  • Fine-tuning around WAN 2.2 (World Action Network) architecture
  • Diffusion sampling schedules and temporal consistency optimization
  • Dataset pairing strategies for high-quality video generation
  • Integration of transformer blocks with video latent modeling
  • Custom training pipelines for domain-specific video generation

Business Applications: Marketing content creation, product demonstrations, training materials, social media automation

2. Text → Motion Graphics & Animation

Technical Implementation:

  • Building on Sora architecture for shorter, high-quality sequences
  • Compositing via ControlNet or AnimateDiff for precise control
  • Advanced prompting techniques for camera motion and typography animation
  • Timing synchronization and keyframe interpolation
  • Integration with existing design workflows

Business Applications: Automated marketing videos, presentation graphics, social media content, brand animations

3. StreamingVLM: Real-Time Video Understanding

Technical Implementation:

  • Inference pipelines for live stream analysis and understanding
  • Continuous memory buffers and real-time evaluation metrics
  • Low-latency multimodal processing architectures
  • Edge deployment for real-time video analytics
  • Integration with existing surveillance and monitoring systems

Business Applications: Security monitoring, quality control, live event analysis, customer behavior tracking

4. Wireframe → Code Generation

Technical Implementation:

  • Vision encoder + code-generation decoder architecture (BLIP → Codex style)
  • Paired datasets: Figma/Screenshot + HTML/CSS/React code
  • Multi-modal understanding of design intent and component relationships
  • Real-world workflow: designer uploads wireframe → model outputs production-ready code
  • Integration with existing development workflows and version control

Business Applications: Rapid prototyping, design-to-development acceleration, automated UI generation

5. Browser Agents (Autonomous Navigation & Control)

Technical Implementation:

  • ReAct-style planning with browser APIs (BrowserGym, AutoGPT WebArena)
  • Action-observation loops with DOM element embeddings
  • Click-path grounding and intelligent navigation strategies
  • Multi-step task completion with error recovery
  • Integration with existing automation and testing frameworks

Business Applications: Web automation, testing, data collection, customer service automation, competitive intelligence

6. LLM Embedded Models (llama.cpp)

Technical Implementation:

  • Model compilation, quantization, and optimization for on-device inference
  • Embedding models as local reasoning backends for browser agents
  • Edge deployment strategies for privacy and performance
  • Integration with existing applications and workflows
  • Custom model fine-tuning for specific use cases

Business Applications: Privacy-focused AI, offline AI capabilities, cost reduction, regulatory compliance

7. Real-Time Text → Voice Systems

Technical Implementation:

  • Low-latency text-to-speech with streaming vocoders (VITS, Bark, Tortoise RT)
  • WebRTC integration for live response streaming
  • Voice cloning and customization capabilities
  • Multi-language and accent support
  • Real-time emotion and tone adjustment

Business Applications: AI receptionists, customer service, accessibility tools, content creation

8. AI Receptionist & Live Interaction Systems

Technical Implementation:

  • End-to-end pipeline: speech-to-text → LLM reasoning → text-to-speech
  • Session memory and context management
  • Speaker diarization and interruption handling
  • Multi-modal interaction (voice, text, visual)
  • Integration with existing phone and communication systems

Business Applications: Customer service automation, appointment scheduling, lead qualification, support desk

9. Data Analysis with Multi-Agent Reasoning

Technical Implementation:

  • Agent architecture: Data Loader → Analyzer → Verifier → Reporter
  • Tool-use and verification loops with frameworks like DSPy or LangGraph
  • Collaborative reasoning and error correction mechanisms
  • Automated insight generation and report creation
  • Integration with existing data infrastructure

Business Applications: Automated reporting, business intelligence, data-driven decision making, compliance reporting

10. Financial Analysis with Multi-Agent Systems

Technical Implementation:

  • Retrieval from financial documents, computation, and reasoning verification
  • Structured output generation with hallucination reduction
  • Self-consistency checks and judge model validation
  • Compliance and audit trail maintenance
  • Integration with existing financial systems and databases

Business Applications: Investment analysis, risk assessment, regulatory compliance, financial planning, audit automation

Core AI/ML Capabilities Our Engineers Master

Foundational AI/ML Techniques

Tokens & Embeddings

  • Text tokenization strategies and optimization
  • Embedding model selection and fine-tuning (Word2Vec, GloVe, BERT)
  • Vector space optimization and dimensionality reduction
  • Custom tokenization for domain-specific applications

Multi-Modal Embeddings

  • CLIP, ImageBind, and unified embedding spaces
  • Cross-modal retrieval and similarity search
  • Multi-modal fusion architectures
  • Custom multi-modal model training

RAG (Retrieval-Augmented Generation)

  • Vector database design and optimization (Pinecone, Weaviate, Chroma)
  • Semantic search and context injection strategies
  • Hybrid search combining dense and sparse retrieval
  • Real-time knowledge base integration

Advanced RAG Systems

  • Multi-hop reasoning and query decomposition
  • Re-ranking and result refinement
  • Contextual compression and relevance filtering
  • Dynamic knowledge graph integration

Web Crawling & Data Collection

  • Large-scale web scraping and data extraction
  • Content parsing, cleaning, and structuring
  • Real-time data pipeline development
  • Compliance with robots.txt and rate limiting

Fine-Tuning & Model Optimization

  • Instructional fine-tuning and RLHF (Reinforcement Learning from Human Feedback)
  • LoRA, QLoRA, and PEFT (Parameter-Efficient Fine-Tuning) techniques
  • Custom dataset creation and curation
  • Model distillation and compression

Multi-Agent Architectures

  • Agent orchestration and communication protocols
  • Tool use and API integration
  • Collaborative reasoning and consensus mechanisms
  • Hierarchical agent systems and delegation

Multi-Hop Reasoning

  • Chain-of-thought and tree-of-thought prompting
  • Reasoning verification and self-correction
  • Complex problem decomposition
  • Logical consistency checking

Model Architectures & Frameworks

Self-Attention & Transformer Architectures

  • Encoder-decoder models and attention mechanisms
  • Custom transformer implementations
  • Attention pattern analysis and optimization
  • Positional encoding strategies

Decoder-Only Architectures

  • GPT-style models and causal language modeling
  • Autoregressive generation optimization
  • Context window management and extension
  • Custom decoder architectures

Neural Network Fundamentals

  • Deep learning architectures (CNNs, RNNs, LSTMs, GRUs)
  • Activation functions and optimization algorithms
  • Regularization techniques and dropout strategies
  • Custom layer implementations

Traditional ML Models

  • N-gram language models and statistical approaches
  • Ensemble methods (Random Forest, XGBoost, LightGBM)
  • Support Vector Machines and kernel methods
  • Bayesian models and probabilistic reasoning

MLOps & Production Infrastructure

Machine Learning Operations (MLOps)

  • Model versioning and experiment tracking (MLflow, Weights & Biases)
  • Automated training and deployment pipelines
  • Model monitoring and drift detection
  • A/B testing frameworks for model performance

Model Optimization & Deployment

  • Quantization, pruning, and knowledge distillation
  • ONNX conversion and cross-platform deployment
  • TensorRT and hardware-specific optimization
  • Edge deployment and mobile optimization

Distributed Training & Scaling

  • Multi-GPU and multi-node training strategies
  • Gradient accumulation and synchronization
  • Data parallelism and model parallelism
  • Distributed inference and serving

Inference Optimization

  • Batching strategies and request optimization
  • Caching and memoization techniques
  • Model serving frameworks (TensorFlow Serving, TorchServe, vLLM)
  • Load balancing and auto-scaling

Domain-Specific Applications

Natural Language Processing (NLP)

  • Text classification, named entity recognition (NER)
  • Sentiment analysis and emotion detection
  • Document summarization and question answering
  • Language translation and multilingual models

Computer Vision

  • Object detection and semantic segmentation
  • Image classification and feature extraction
  • Optical Character Recognition (OCR)
  • Medical imaging and diagnostic systems

Time Series Forecasting

  • LSTM and Transformer-based forecasting models
  • Anomaly detection and pattern recognition
  • Financial modeling and risk assessment
  • IoT sensor data analysis and predictive maintenance

Recommendation Systems

  • Collaborative filtering and matrix factorization
  • Content-based filtering and hybrid approaches
  • Deep learning recommendations (Neural Collaborative Filtering)
  • Real-time recommendation engines

AI/ML Success Stories

Case Study: AI-Powered Customer Service Automation

Our AI engineers built an intelligent customer service system for a Fortune 500 company that reduced support costs by 40% annually while boosting CSAT by 28% and handling 70% of inquiries autonomously. The system combined speech recognition, natural language understanding, and multi-agent reasoning to handle 85% of customer inquiries without human intervention.

Case Study: Computer Vision Quality Control

We staffed ML engineers for a manufacturing giant who developed a computer vision system that increased defect detection accuracy from 75% to 99% while significantly reducing inspection time. The system processes tens of thousands of products daily and has prevented millions in potential recalls and warranty claims.

Case Study: Financial Fraud Detection

Some of our AI Engineers built a real-time fraud detection system for a fast growing unicorn that reduced fraudulent transaction volume by 68% and lowered false positives by 45% within the first year of deployment. The system integrates transaction history modeling, device fingerprinting, and behavioral biometrics, enabling sub-100ms decision latency across millions of daily transactions.

Why Companies Choose Our AI/ML Engineers

🚀 Proven AI Implementation Track Record

$2B+ Value Created
Through AI implementations
Average 50% Cost Reduction
Within first 12 months
98% Production Success
Model deployment rate

AI/ML Engineering Specializations

  • Large Language Models (LLMs) - Fine-tuning, prompt engineering, RAG systems
  • Computer Vision - Object detection, image generation, medical imaging, autonomous systems
  • Natural Language Processing - Sentiment analysis, document processing, conversational AI
  • Generative AI - Text generation, image synthesis, video creation, content automation
  • MLOps & Infrastructure - Model deployment, monitoring, scaling, version control
  • Multi-Modal AI - Vision-language models, audio-visual processing, cross-modal understanding
  • Edge AI & Optimization - Model compression, mobile deployment, real-time inference
  • AI Agents & Automation - Autonomous systems, workflow automation, decision-making systems

Technology Stack Expertise

AI/ML Frameworks

  • PyTorch, TensorFlow, JAX, Hugging Face Transformers
  • LangChain, LlamaIndex, DSPy, AutoGen
  • OpenAI API, Anthropic Claude, Google Vertex AI
  • Stable Diffusion, DALL-E, Midjourney APIs

Cloud & Infrastructure

  • AWS (SageMaker, Bedrock, Lambda), Google Cloud (Vertex AI, AutoML)
  • Azure (ML Studio, Cognitive Services), Databricks
  • Docker, Kubernetes, MLflow, Weights & Biases
  • Vector databases (Pinecone, Weaviate, Chroma, Qdrant)

Programming & Tools

  • Python (NumPy, Pandas, Scikit-learn), R, Julia
  • CUDA, OpenCL for GPU acceleration
  • Apache Spark, Ray for distributed computing
  • Git, DVC for version control and data management

Pricing & Engagement Models

Fully-Burdened AI/ML Engineering Costs

  • AI Engineer: $5,000-$15,000/month
  • ML Engineer: $4,500-$12,000/month
  • Prompt Engineer: $4,000-$10,000/month
  • MLOps Engineer: $5,500-$15,500/month

All-inclusive pricing covers base salary, benefits, payroll, taxes, legal, HR, insurance, recruiting, and other overhead. Pricing varies based on experience level, specialization, and location.

Flexible Engagement Options

  • Individual Contributors - Single AI/ML engineer or specialist
  • AI Development Pods - 2-4 person cross-functional teams
  • Full AI Teams - Complete AI organizations (5-20 people)
  • Project-Based - Specific AI implementations with defined deliverables
Build Your AI Team

AI/ML Engineering Staffing FAQs

What's the difference between an AI Engineer and an ML Engineer?
AI Engineers focus on building end-to-end AI applications and integrating AI capabilities into products, while ML Engineers specialize in training, optimizing, and deploying machine learning models with emphasis on MLOps and infrastructure. AI Engineers are more product-focused, while ML Engineers are more model and pipeline-focused. Most successful AI projects benefit from both roles working together.
How do you vet AI/ML engineers for cutting-edge skills?
We evaluate candidates through hands-on technical assessments including model implementation, prompt engineering challenges, and real-world problem-solving scenarios. Our vetting process includes portfolio reviews of production AI systems, technical interviews with our AI experts, and practical coding exercises using modern frameworks like PyTorch, Hugging Face, and LangChain.
Can your engineers work with our existing ML infrastructure?
Absolutely. Our AI/ML engineers have extensive experience integrating with existing infrastructure including cloud platforms (AWS, GCP, Azure), MLOps tools (MLflow, Kubeflow, Weights & Biases), and data pipelines. They can work within your current tech stack or help modernize and optimize your AI infrastructure.
Do your AI engineers have experience with large language models and generative AI?
Yes, our engineers have hands-on experience with LLMs including GPT, Claude, Llama, and open-source models. They’re skilled in fine-tuning, prompt engineering, RAG implementation, and building production applications with LLM APIs. Many have contributed to cutting-edge generative AI projects including text-to-image, text-to-video, and multi-modal applications.
How quickly can an AI/ML engineer start on our project?
For general AI/ML roles, we can typically match you with qualified engineers within 1-2 weeks. For highly specialized roles (like computer vision experts or LLM fine-tuning specialists), it may take 2-4 weeks to find the perfect fit. Our engineers can start contributing to your project immediately after onboarding.
Can you help with both research and production AI implementations?
Yes, we staff engineers for both research and production environments. Our research-focused engineers excel at experimentation, paper implementation, and proof-of-concept development, while our production engineers specialize in scalable deployment, optimization, and MLOps. We can provide teams that bridge both research and production needs.
Do your engineers stay current with the latest AI developments?
Our AI/ML engineers are required to stay current with the rapidly evolving AI landscape. They regularly work with the latest models, frameworks, and techniques. We provide continuous learning opportunities and encourage participation in AI conferences, research communities, and open-source projects to ensure they remain at the cutting edge.
What industries do your AI/ML engineers have experience in?
Our engineers have domain expertise across finance, healthcare, e-commerce, manufacturing, transportation, energy, automotive, entertainment, and more. We match engineers based on both technical skills and relevant industry experience to ensure they understand your specific business context and regulatory requirements.

Ready to transform your business with AI? Our AI and ML engineers have the expertise to turn your AI vision into production reality, whether you’re building your first AI feature or scaling to enterprise-level AI systems.

Start Your AI Project

We match great companies with great AI talent. Our global team of AI/ML engineers work in your time zone, communicate in English, and cost up to 50% less than US-based AI specialists while delivering world-class results.

Related Services: Looking for complementary expertise? Check out our Data Scientists & Data Engineers for data pipeline and analytics support, or our Enterprise Software Developers for full-stack application development to integrate your AI capabilities, or our DevOps & SRE Engineers for cloud infrastructure and MLOps support.

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