Help us deploy real AI systems into production. You'll sit at the intersection of software engineering, ML, cloud infrastructure, and agentic AI — turning prototypes into reliable services that clients actually use, measure, and trust.
BorionAI is a boutique AI implementation firm built around one principle: AI should not stop at prototypes, notebooks, or slide decks. We deploy production-ready AI systems that work in real business environments, on timelines measured in weeks, and we tie our compensation to delivery — not hours billed.
We work with enterprises tired of long consulting engagements, stalled pilots, and AI projects that never reach production. Our model is different: small senior teams, fast execution, clear ownership, fixed-scope delivery, and practical systems that connect data, models, APIs, workflows, and business users.
We are not a strategy firm, a staff-augmentation shop, or a slide-deck factory. We are an implementation firm.
Why work with us
A 100% remote role for people who like building real systems. You'll work directly on production AI, ML, and Agentic AI solutions instead of getting stuck in layers of process. The work is hands-on, technical, and outcome-driven: pipelines, APIs, model endpoints, service layers, cloud deployments, orchestration, CI/CD, monitoring, and client-facing AI products.
Deployment, deployment, deployment — production-ready systems delivered in less than 90 days whenever scope allows.
BorionAI is a senior, execution-focused team. High standards, low bureaucracy, clear delivery mindset. We're looking for senior builders who want ownership — room to design, deploy, simplify, and make technical decisions that directly affect delivery.
Role summary
This role sits at the intersection of Software Engineering, Machine Learning Engineering, MLOps, cloud infrastructure, and Agentic AI deployment. You'll work closely with Data Scientists and AI Engineers to turn models, pipelines, and prototypes into reliable production systems.
The ideal candidate isn't just someone who knows ML tools. We need someone who can build the production layer around AI: APIs, orchestration, model endpoints, cloud data flows, CI/CD, logging, monitoring, authentication, scalability, and service design.
What you will do
Build and deploy production-grade ML and Agentic AI pipelines
Design data flows across S3, ADLS, GCS and other cloud storage
Build the service layer between ML, APIs, web apps, and client tools
Test and document APIs with Postman or similar
Build CI/CD pipelines for ML and software services
Deploy to AWS, Azure, or GCP environments
Productionize models — close the gap between notebooks and systems
Work under pressure with clear timelines and delivery commitments
Design and maintain workflows using Airflow, Prefect, Dagster, or similar
Expose ML models via batch and real-time inference endpoints
Build REST APIs for model access, predictions, and integrations
Implement authentication, logging, monitoring, and observability
Containerize and deploy services with Docker; Kubernetes when needed
The strongest candidates can explain trade-offs clearly, simplify complex systems, and connect data, models, APIs, cloud infrastructure, and client-facing applications into one reliable production workflow.
Tool knowledge matters — but ownership, engineering judgment, fluent communication, consulting maturity, and production experience matter more.
Engineers who have built real systems end-to-end.
Ready to ship real AI?
Send your CV and a short note on why this role fits to the address below. We read every application.