AI Developer - Senior | Remote

Related Industries

Artificial Intelligence, Technology, SaaS, Startups, Digital Products, FinTech, HealthTech, E-commerce.

Purpose

We work with startups and technology-driven companies that want to put AI to work - not just add a chatbot and call it innovation.

This job is for AI Developers who enjoy turning AI capabilities into useful, reliable products; from understanding the customer and the problem, to choosing the right approach, building the solution, integrating it into real systems, and making sure it actually works in production.

How You’ll Make an Impact

• Build AI that actually ships. Design, develop, test, and deploy scalable, secure, and maintainable AI applications using Generative AI, LLMs, RAG, and modern AI frameworks.
• Turn customer problems into practical AI solutions. Understand what the customer and business actually need, identify where AI adds value, and translate those needs into reliable technical solutions.
• Engineer production-ready AI services and reusable components using Python, REST APIs, FastAPI, and microservice-based architectures.
• Design end-to-end architectures that connect AI capabilities with enterprise applications, data platforms, identity services, APIs, and business workflows.
• Build cloud-native AI solutions across AWS, Azure, or GCP, selecting services based on scalability, security, performance, operational requirements, and cost.
• Manage the full lifecycle of AI applications, from data ingestion and preprocessing through orchestration, API integration, deployment, monitoring, and ongoing optimization.
• Build and maintain CI/CD pipelines for AI applications, model components, infrastructure, testing, and production releases.
• Apply strong software engineering practices including modular design, automated testing, code reviews, Git, observability, secure coding, documentation, and maintainable architecture.
• Implement the less-glamorous-but-very-important parts of production AI: guardrails, access controls, evaluations, logging, monitoring, reliability, latency, cost management, and troubleshooting.
• Maintain, troubleshoot, and improve existing AI solutions so they remain reliable, available, performant, and useful after the demo is over.
• Work with product managers, architects, data engineers, application teams, cybersecurity teams, and business stakeholders to turn requirements into robust technical solutions.
• Lead technical discussions, evaluate architectural alternatives, and communicate engineering decisions, dependencies, risks, and trade-offs clearly.
• Mentor other engineers through pair programming, design guidance, code reviews, technical coaching, and knowledge sharing.
• Promote reusable engineering patterns across AI development, MLOps, DevOps, cloud architecture, and secure software delivery.
• Keep an eye on the rapidly evolving AI ecosystem. Research new models, frameworks, agents, multimodal capabilities, and engineering approaches, and know the difference between something genuinely useful and the latest shiny AI toy.
• Be pragmatic. Sometimes the answer is an agentic architecture with five services and a vector database. Sometimes it’s a well-designed API and a good prompt. Your job is to know the difference.
• Own the outcome, not just the model. A technically impressive AI system that nobody can use, maintain, afford, or trust isn’t a successful solution.

Compensation DOE (Dependent on Experience).