Lead AI Engineer

As a Lead AI Engineer, you will design and build applied AI solutions that drive measurable business value from concept through scalable production deployment. You'll architect enterprise AI systems leveraging large language models, retrieval-augmented generation, and agentic workflows while leading technical strategy and mentoring engineering teams. Key Responsibilities Design and architect AI-powered systems using LLMs, RAG, agentic workflows, and orchestration patterns integrated with enterprise data and business processes Develop secure, maintainable, production-ready software platforms and cloud-native services that orchestrate models, tools, retrieval systems, and enterprise workflows Build rapid prototypes and proof-of-concepts to validate new technologies and identify business opportunities Establish comprehensive evaluation, monitoring, and quality practices including testing, benchmarking, observability, and continuous improvement Lead technical design discussions, architecture reviews, and drive engineering best practices across teams Mentor engineers and develop reusable AI capabilities and frameworks that accelerate delivery across the organization Collaborate with product teams, architects, domain experts, customers, and partners to identify opportunities and deliver business impact Influence IFS's AI strategy and long-term technology direction through hands-on delivery, experimentation, and customer engagement, including external-facing innovation through industry events and partner collaboration Core Requirements  Bachelor’s degree in computer science, Software Engineering, AI, Data Science, or a related field. Master's degree is advantageous.  8+ years of professional experience in AI, Machine Learning, and/or Software Engineering, backed by a proven track record of successfully delivered projects.  Experience bringing incubated AI solutions to production, including scoping, design, development, testing, deployment, and vigilant monitoring.  Strong programming skills one or more mainstream programming languages such as Python, Golang, C# or TypeScript.  Experience with context engineering, including retrieval architecture, embeddings, vector databases, search technologies, and retrieval optimization techniques.  Strong backend engineering fundamentals, including APIs, distributed services, cloud-native architectures, CI/CD, integration, automation, and security.  A solid background in DevOps and MLOps/LLMOps practices, and familiarity with tools to manage infrastructure as code, like Terraform and package managers like Helm Charts.  Ability to design solutions that integrate enterprise applications, business processes, workflows, and data platforms.  Applied AI & Architecture  Experience designing and implementing AI-driven architectures using LLMs, retrieval-augmented generation (RAG), agentic workflows, orchestration patterns, and enterprise data…