AI Engineer
Our customer design and deliver bespoke AI solutions that combine state-of-the-art models with robust, production-grade engineering. We don’t believe AI is magic—but when it’s built thoughtfully and executed well, it can feel that way. We’re seeking a hands-on builder who is excited to push the boundaries of what AI can do, while grounding innovation in strong full-stack engineering principles.
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What You’ll Do
Design, prototype, and scale AI-native applications and agent-based systems that drive real business outcomes.
Work end-to-end across the stack, including front-end development (React, TypeScript), backend services (Python, Node.js, Go), APIs, and data stores (SQL, NoSQL, and vector databases).
Build and optimize LLM-driven workflows, leveraging techniques such as retrieval-augmented generation (RAG), embeddings, multi-agent orchestration, and effective context management.
Architect, deploy, and maintain infrastructure, including CI/CD pipelines, Kubernetes, cloud services, and observability tooling.
Move efficiently from proof-of-concept to production, balancing speed with scalability, security, and long-term maintainability.
Continuously optimize AI systems for accuracy, performance, latency, and cost efficiency.
Partner closely with customers, engineers, product managers, and designers to translate experimentation into reliable, production-ready features.
Stay hands-on with modern, developer-first tools such as Cursor, Claude Code, GitHub Copilot, and similar platforms to maximize productivity.
About You
5+ years of professional software engineering experience, including at least 2 years building AI-powered systems.
Strong full-stack background, with experience in modern front-end frameworks (React, TypeScript), backend development (Python, Node.js, Go), and a range of databases (SQL, NoSQL, vector stores).
Familiarity with AI and LLM development tools such as Cursor, Claude Code, GitHub Copilot, LangChain, CrewAI, or comparable frameworks.
Hands-on experience with cloud-native architectures (AWS, Azure, or GCP), Kubernetes, Docker, CI/CD workflows, monitoring, and scalable systems.
Solid understanding of the LLM lifecycle, including prompting strategies, evaluation, fine-tuning, embeddings, RAG, and agent design.
A pragmatic engineering mindset—you recognize that reliable AI systems require testing, observability, safeguards, and fallback logic, not just clever prompts.
Strong communication and collaboration skills, with the ability to bridge technical depth and business context. xsgimln
Curiosity and enthusiasm for exploring new ideas, paired with a commitment to delivering production-quality software.