HBE is your offshore AI developers staffing agency for building high-performance, cost-effective AI, machine learning, and data science teams. We source remote AI engineers, offshore ML developers, and nearshore data experts who plug directly into your product, platform, or enterprise roadmap.
Whether you need a single LLM Engineer or a full offshore AI squad, HBE delivers vetted, English-speaking talent, time-zone aligned, ready to ship code and ship features.
Lower cost per senior AI developer vs local hiring
Global talent pools (offshore & nearshore) across AI, ML, data, and MLOps
Flexible staff augmentation, dedicated teams, or project-based models
Fast ramp-up: from role definition to interviews and onboarding
Experience placing developers for SaaS, fintech, e-commerce, logistics, industrial, and manufacturing AI
HBE can staff everything from a single Machine Learning Engineer to a full offshore AI department—including LLM Engineers, RAG Engineers, MLOps specialists, AI Product Managers, and AI leadership.
Build Your Offshore AI Team with HBE
Our AI Engineers and Generative AI Engineers integrate models into real products and customer experiences.
Build AI-powered features using OpenAI, Anthropic, Claude, Gemini, local LLMs
Implement chatbots, copilots, content generation, code assistants
Work with APIs, vector databases (Pinecone, Weaviate, Qdrant), and orchestration tools
For heavy deep learning workloads—vision, speech, large models—we staff Deep Learning Engineers with hands-on GPU experience.
Architect and train CNNs, RNNs, Transformers, diffusion models
Optimize training and inference on GPUs
Work with PyTorch, TensorFlow, JAX, and modern DL toolchains
For heavy deep learning workloads—vision, speech, large models—we staff Deep Learning Engineers with hands-on GPU experience.
Architect and train CNNs, RNNs, Transformers, diffusion models
Optimize training and inference on GPUs
Work with PyTorch, TensorFlow, JAX, and modern DL toolchains
Offshore Computer Vision Engineers for robotics, manufacturing, security, and medical imaging.
Image and video processing, object detection, segmentation, tracking
Implement vision for quality control, defect detection, OCR, and automation
Deploy models to edge devices, cameras, and embedded systems
LLM Engineers are the core of modern AI applications.
Design prompt pipelines, agents, and tools
Fine-tune and customize open-source and proprietary LLMs
Integrate LLMs into web apps, mobile apps, and internal tools
Work with RAG, vector search, function calling, and tools
For next-generation autonomous AI agents that perform tasks end-to-end.
Build multi-step agents for research, outreach, operations, and workflows
Orchestrate tools, APIs, and external systems using agent frameworks
Monitor, guardrail, and evaluate agent performance and safety
Prompt Engineers specialize in controlling the behavior of LLMs through carefully designed prompts, templates, and flows.
Design prompt libraries optimized for accuracy and consistency
Create system prompts, role prompts, and dynamic prompt chains
Collaborate with product and UX to build LLM-powered experiences
RAG Engineers connect LLMs to enterprise data for accurate, grounded answers.
Implement document ingestion pipelines
Design chunking, embeddings, and vector search strategies
Build knowledge assistants on top of SharePoint, Confluence, CRMs, ERPs
To run AI at scale, you need strong MLOps and ML platform engineering.
Design CI/CD pipelines for machine learning models
Manage model registries, model deployment, monitoring, and retraining
Work with tools like Kubeflow, MLflow, Airflow, SageMaker, Vertex AI
Our Data Scientists transform messy real-world data into actionable insights and ML-ready features.
Exploratory data analysis, dashboards, and advanced analytics
Build predictive models, churn models, pricing models, and risk scoring
Communicate results in clear, business-friendly language
Our Data Scientists transform messy real-world data into actionable insights and ML-ready features.
Exploratory data analysis, dashboards, and advanced analytics
Build predictive models, churn models, pricing models, and risk scoring
Communicate results in clear, business-friendly language
For AI initiatives to succeed, you need AI Product Managers who understand both business and models.
Define AI product vision, roadmap, and success metrics
Translate use cases into model requirements and user stories
Coordinate between engineering, data, design, operations, and leadership
Build out your entire AI function with offshore or hybrid leadership.
Own the AI roadmap, team structure, and hiring plan
Establish best practices for ML, MLOps, governance, and ethics
Align AI initiatives with C-suite and board-level priorities
For organizations making a strategic investment in AI, a VP of AI or VP of ML is key.
Drive enterprise-wide AI transformation
Oversee budgets, partnerships, and major AI programs
Represent AI strategy to investors, partners, and key stakeholders
A Chief AI Officer (CAIO) sits at the intersection of technology, strategy, and risk.
Define company-wide AI strategy and governance frameworks
Ensure AI is used responsibly, securely, and compliantly
Integrate AI into every major business unit and product line