Artificial Intelligence Engineer

AI Engineer – Opportunity Overview We are seeking an AI Engineer with a strong software engineering foundation who can build and deploy production-ready AI solutions. This is not a traditional Data Scientist role. The ideal candidate is a full-stack or backend-focused software engineer who has hands-on experience working with LLMs, Retrieval-Augmented Generation (RAG), agentic AI frameworks, and AI-powered applications. Success in this role comes from combining strong engineering fundamentals with modern AI development practices. We're looking for someone who understands how to move AI solutions beyond experimentation and proof-of-concepts into scalable, secure, and maintainable production environments. Experience implementing agents, integrating tools and APIs, managing context and memory, and supporting enterprise-scale deployments is highly valued. What We're Looking For Strong Software Engineering Foundation This role prioritizes engineering expertise first and AI experience second. Ideal candidates will have: • Full-stack or backend software development experience • Experience building, deploying, and supporting production applications • Strong understanding of scalable application design and software architecture • Experience maintaining reliable, secure, and supportable solutions • Ability to troubleshoot, optimize, and improve complex systems Note: A strong software engineer with practical AI experience is generally preferred over a highly theoretical AI specialist without proven engineering experience. AI & Generative AI Experience Candidates should have experience with: • Large Language Models (LLMs) • Retrieval-Augmented Generation (RAG) • Agentic AI concepts and frameworks • AI workflow orchestration • Integrating AI capabilities into enterprise applications • Designing solutions that leverage modern generative AI technologies Preference will be given to candidates with recent, hands-on experience building solutions utilizing current-generation LLM technologies. Agentic AI Development This role focuses primarily on implementing and operationalizing agent-based solutions rather than designing foundational AI platforms from scratch. Responsibilities may include: • Implementing agents using established frameworks and technology stacks • Connecting and orchestrating multiple AI services and agents • Integrating agents with enterprise systems, tools, and APIs • Building repeatable and reliable AI automation workflows • Supporting production deployment and operational readiness Candidates should understand: • How multiple agents and services interact • Common challenges associated with agent reliability and consistency • Approaches for building resilient AI-driven workflows • Current trends and evolving practices within agentic AI development LLM Engineering vs. Prompt Engineering While prompt engineering is useful, it represents only a small portion of this role. Greater emphasis is placed on: • Context engineering • Memory management • Agent planning and behavior • Tool utilization and orchestration • Observability and monitoring • Security and authentication • Production deployment considerations • System reliability and scalability We are seeking engineers who understand how AI systems function in real-world environments rather than individuals whose experience is limited to prompt creation. MCP & Tool Integration Experience Experience with tool-connected AI systems is highly desirable. Candidates should understand: • Model Context Protocol (MCP) • Integrating AI agents with external tools and services • API integrations and enterprise system connectivity • Authentication and authorization patterns • Security considerations surrounding tool-enabled agents • How AI systems interact with business applications and data sources Production AI Experience A key differentiator for this position is experience moving AI solutions from prototype to production. The ideal candidate has experience with: • Taking AI applications from concept through deployment • Production support and operational ownership • Monitoring, observability, and troubleshooting • Scalability and performance optimization • Reliability, testing, and governance practices • Enterprise-grade deployment considerations We're particularly interested in candidates who have delivered real-world AI solutions rather than solely participating in experimental projects or proofs of concept. Business & Functional Understanding While domain expertise is valuable, the primary focus is finding a strong technical engineer who can quickly learn business processes and customer needs. The ideal candidate brings: • Strong software engineering skills • Practical AI/LLM implementation experience • Ability to translate business requirements into technical solutions • Strong communication and collaboration skills • Willingness to learn new domains and business processes