Lead AI Engineer

Lead AI Engineer📍 Location: New York, United States (Hybrid)🏢 Industry: Financial Services💼 Work Setting: HybridAre you passionate about building next-generation AI solutions, scaling production-grade machine learning systems, and transforming cutting-edge research into real business impact? We are seeking an experienced Lead AI Engineer to drive the design, development, deployment, and optimization of advanced artificial intelligence solutions that solve complex business challenges and create measurable enterprise value.In this role, you will lead the end-to-end delivery of AI-powered applications, including generative AI, large language models (LLMs), intelligent agents, machine learning systems, and advanced analytics solutions. You will work closely with business leaders, engineers, and data professionals to translate innovative ideas into scalable, production-ready solutions while establishing best practices in AI engineering, governance, and responsible AI adoption.Key ResponsibilitiesAI Solution Architecture & DeliveryDesign, develop, and deploy end-to-end AI and machine learning solutions from concept through production implementation.Architect scalable, reliable, and maintainable AI platforms and applications that support enterprise business objectives.Lead the development of generative AI, intelligent agent frameworks, machine learning models, and predictive analytics solutions.Ensure AI systems meet performance, reliability, scalability, security, and governance requirements.Oversee operational monitoring, model lifecycle management, and continuous optimization of deployed solutions.Research, Experimentation & InnovationEvaluate emerging AI technologies, frameworks, and methodologies to identify opportunities for innovation.Conduct experiments, benchmarking, model comparisons, and performance evaluations to validate approaches and inform technical decisions.Translate advanced research concepts into practical applications that deliver measurable business outcomes.Build prototypes and proof-of-concepts to rapidly assess new AI capabilities and business opportunities.Drive adoption of modern AI engineering practices and continuous improvement initiatives.Machine Learning & Advanced AnalyticsDevelop and optimize machine learning, natural language processing, predictive modeling, and statistical solutions.Design evaluation frameworks and performance measurement methodologies for AI systems.Apply advanced analytical techniques to solve complex business and operational challenges.Leverage a variety of AI and data science approaches, selecting the most appropriate solution for each use case.Develop robust AI workflows that support experimentation, governance, and business adoption.Engineering & Platform DevelopmentBuild production-quality software and AI services using modern engineering practices.Develop APIs, automation workflows, and scalable AI pipelines that integrate with enterprise systems.Collaborate with engineering teams to deploy and maintain AI solutions within cloud and enterprise environments.Support containerization, orchestration, deployment automation, and infrastructure modernization efforts.Ensure code quality through testing, documentation, peer reviews, and engineering best practices.Leadership & Stakeholder EngagementPartner with business leaders and stakeholders to identify opportunities where AI can create strategic value.Translate complex technical concepts, methodologies, and results into clear business recommendations.Influence strategic decisions through data-driven insights and AI-enabled innovation.Lead technical discussions and guide enterprise AI strategy and roadmap development.Act as a trusted advisor on emerging technologies and enterprise AI adoption.Mentorship & Technical ExcellenceMentor and develop team members while fostering a culture of innovation, learning, and technical excellence.Establish engineering standards, AI governance practices, and development frameworks.Promote knowledge sharing and cross-functional collaboration across technical teams.Support talent development and contribute to building a high-performing AI organization.Required QualificationsMaster's degree or Ph.D. in Computer Science, Data Science, Machine Learning, Statistics, Applied Mathematics, Engineering, Physics, or a related quantitative field.Minimum of 7 years of experience in AI engineering, machine learning, data science, or advanced analytics roles.Deep expertise in machine learning, statistics, natural language processing, large language models, and generative AI technologies.Proven experience designing, building, deploying, and scaling production AI solutions.Strong programming expertise in Python and modern software engineering practices.Experience developing enterprise-grade applications and APIs.Knowledge of cloud-native architectures, containerization, orchestration, and deployment frameworks.Strong analytical, problem-solving, and decision-making capabilities.Exceptional communication skills with the ability to explain complex technical concepts to diverse audiences.Preferred QualificationsExperience building intelligent agent systems, AI orchestration frameworks, and autonomous workflow solutions.Experience in regulated industries requiring strong governance, privacy, and compliance controls.Knowledge of classical machine learning, causal inference, Bayesian modeling, optimization techniques, and advanced statistical methods.Experience with cloud platforms, modern data architectures, vector databases, semantic search, and retrieval systems.Strong SQL and database design experience.Familiarity with responsible AI practices, AI governance frameworks, and model risk management.Published research, open-source contributions, or demonstrated innovation in artificial intelligence and machine learning.What Success Looks LikeDelivering scalable AI solutions that generate measurable business impact.Successfully deploying production-grade AI applications that are reliable, secure, and maintainable.Driving innovation through emerging AI technologies and advanced analytics.Influencing strategic decisions through data-driven insights and technical expertise.Establishing best practices in AI engineering, governance, and responsible AI adoption.Mentoring high-performing teams and fostering a culture of continuous learning and experimentation.