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We are seeking a Lead AI Engineer to spearhead the design and architecture of enterprise-grade AI agentic solutions. In this role, you will drive innovation across LLMs, foundation models, and multi-agent systems while shaping production AI platforms and fostering a high-performance engineering culture. You will collaborate with cross-functional teams to deliver measurable business impact through cutting-edge AI technologies.
Responsibilities
- Lead, design and architect enterprise-grade AI agentic solutions using LLMs, foundation models and multi-agent systems
- Implement production AI platforms with modular pipelines, multi-source knowledge fusion and governance frameworks
- Collaboration with product, engineering and business teams to understand requirements and drive strategic decisions
- Architecture of hybrid AI systems combining agentic AI, traditional ML, symbolic reasoning and knowledge graphs
- Perform detailed analysis of business problems and design comprehensive solutions with measurable ROI
- Establish MLOps/AIOps practices including continuous evaluation, A/B testing, model versioning and automated retraining pipelines
- Lead code and architecture reviews, ensuring solutions meet security, compliance and best practice standards
- Foster a high-performance AI engineering culture, mentor team members and provide technical leadership
- Design evaluation frameworks covering technical, human-centered, temporal and contextual metrics
- Drive innovation and stay current with emerging AI technologies and research
- Author comprehensive technical documentation, architecture decision records and knowledge base articles
Requirements
- 5+ years of experience in AI/ML engineering with proven leadership in architecting enterprise-grade solutions
- Expertise in LLMs, foundation models and multi-agent systems
- Proficiency in designing production AI platforms with modular pipelines and governance frameworks
- Background in hybrid AI systems combining agentic AI, traditional ML and symbolic reasoning
- Knowledge of knowledge graphs and multi-source knowledge fusion
- Competency in MLOps/AIOps practices including A/B testing, model versioning and automated retraining
- Capability to design evaluation frameworks covering technical, human-centered and contextual metrics
- Understanding of business problem analysis and translating requirements into solutions with measurable ROI
- Showcase of strong cross-functional collaboration and stakeholder management
- Familiarity with security, compliance standards and emerging AI research trends
- English proficiency at B2 level or higher