Lead AI Engineer

Lead AI Engineer Location: New York, NY (Hybrid) Overview An opportunity is available for an experienced Lead AI Engineer to design, build, and scale advanced AI solutions across enterprise environments. This role focuses on developing production-grade generative AI, agentic AI, and large language model (LLM) applications that solve complex business challenges. The successful candidate will combine strong engineering expertise with scientific rigor to deliver reliable, scalable AI systems while mentoring technical teams and influencing AI strategy. Key Responsibilities Architect, develop, and deploy end-to-end AI solutions using large language models, agentic AI, machine learning, and probabilistic modelling. Design and execute rigorous evaluations of AI system performance through experimentation, benchmarking, and quantitative analysis. Research emerging AI technologies and translate innovations into practical enterprise solutions. Develop rapid prototypes and convert successful concepts into production-ready AI applications. Build scalable AI pipelines and APIs that integrate with enterprise technology platforms. Collaborate with engineering, data, and business stakeholders to deliver AI-driven solutions. Communicate technical concepts and recommendations to senior leadership and non-technical stakeholders. Establish best practices for AI development, responsible AI implementation, and engineering standards. Mentor junior engineers and foster a culture of technical excellence and continuous learning. Requirements Minimum 7 years' experience in AI engineering, machine learning, or data science with a proven track record of delivering production AI solutions. Strong expertise in: Machine Learning Statistics Natural Language Processing (NLP) Large Language Models (LLMs) Generative AI Agentic AI architectures Prompt engineering LLM evaluation methodologies Experience designing, deploying, and maintaining production AI systems. Advanced Python programming skills with experience writing production-quality code. Experience with Docker, Kubernetes, and containerised deployment environments. Master's or PhD in Computer Science, Statistics, Applied Mathematics, Electrical Engineering, Physics, or a related quantitative discipline. Preferred Experience Experience with agentic AI platforms and frameworks such as AWS Bedrock AgentCore, AWS Strands, Azure AI, MCP, or A2A protocols. Experience developing AI solutions within regulated industries. Knowledge of classical machine learning, causal inference, optimisation, and Bayesian modelling techniques. Strong SQL skills and experience working with cloud-native data platforms, vector databases, and semantic search technologies. Demonstrated ability to explain complex AI concepts to both technical and non-technical audiences. Research publications, significant open-source contributions, or evidence of applied scientific research in industry. What's Offered Opportunity to work on enterprise-scale AI initiatives using cutting-edge technologies. Collaborative environment alongside experienced AI, machine learning, and data science professionals. Ongoing learning and professional development opportunities. Exposure to high-impact projects that shape AI strategy and innovation across the organisation. Inclusive workplace culture with opportunities for career growth and technical leadership. Show more Show less