Applied AI engineers
Hire AI/ML engineers for applied model-powered products.
Bring in engineers who can build AI features, data pipelines, evaluation loops, and ML-backed product workflows with software-engineering discipline.
AI/ML talent for teams moving from demo to product
Use this path when you need engineers who understand both software delivery and model workflows, from data preparation to evaluation and deployment.
Applied AI beyond demos
- Turn AI prototypes into product features that connect to real users, data, and permissions.
- Useful for fintech, support, education, marketplace, and internal-tool workflows.
- Support data preparation, model integration, monitoring, and measurable iteration.
AI workflows around real operating data
- Design ranking, matching, review, and recommendation logic around structured product data.
- Evaluate outputs against product rules instead of generic model demos.
- Use engineers who understand pipelines, edge cases, and measurable acceptance criteria.
Production-minded AI builders
- Connect AI workflows to APIs, databases, dashboards, care tools, and internal operations.
- Validate quality, latency, privacy, observability, and cost tradeoffs.
- Move from impressive prototype to reliable product behavior.
Shipped work
AI and evaluation examples
Examples where AI is part of a shipped workflow, not a standalone demo.
AfroChat
AI chat productPersona-driven AI chat, multilingual usage, and conversational UX built around real user workflows.
Delivered
Pan-African AI chat across web and mobile, with language-aware routing and model selection.
Public outcome
Catalog metrics show 85 countries, 16 AI models, and 71,000+ messages.
SkillBridge
AI exam prepAI tutoring, personalized plans, contests, and student learning loops that require measurable output quality.
Delivered
AI-powered exam prep with curriculum-aligned material, mock exams, contests, and student learning workflows.
Public outcome
Catalog metrics show 2,700+ students, 12,000+ topics covered, and 75,000+ questions solved.
Engram
AI developer toolsPersistent memory for coding agents, focused on team knowledge, recall, and AI-assisted engineering workflows.
Delivered
AI developer-tool workflow for persistent agent memory through an MCP-compatible shared knowledge layer.
Public outcome
Public site positions Engram as an open-source MCP server with store, recall, learn, and context tools for AI coding agents.
Need AI talent plus product engineering?
AI work rarely stops at a model. We can pair AI/ML engineers with backend, frontend, product, or evaluation capacity.
Fullstack Developers
Hire builders who can move across frontend, backend, APIs, databases, and deployment.
Skills
Frontend Developers
Bring in engineers for polished interfaces, dashboards, design systems, and fast web apps.
Skills
Backend Developers
Build reliable APIs, integrations, data models, services, and cloud-backed systems.
Skills
Mobile Developers
Ship iOS, Android, Flutter, and React Native apps with product-quality execution.
Skills
How we match AI/ML engineers
We screen for practical ML knowledge, data judgment, coding ability, and product execution.
Scoped role
Vetted shortlist
Trial checkpoint
Define the AI workflow
We clarify the model use case, data sources, evaluation criteria, integration points, and production constraints.
Match applied AI builders
We shortlist engineers with relevant Python, ML, data pipeline, and product integration experience.
Validate with measurable output
The trial checks code quality, experiment discipline, evaluation thinking, and ability to ship AI features responsibly.
FAQ
Hiring AI/ML Engineers: FAQ
Still unsure which role fits? Send the context and we will map the cleanest hiring path.