Top AI & Machine Learning Jobs

Lead end-to-end analytics for Controls Room: design, build, validate, and productionize advanced models (forecasting, anomaly detection, segmentation, optimization, LLM-enhanced solutions). Ensure data readiness, create scalable pipelines, embed governance and traceability, produce reusable assets, and communicate actionable insights to technical and non-technical stakeholders.

Design, build, and deploy production-grade LLM-powered applications and agent systems; implement RAG, integrate with cloud services and data stores; apply secure-by-design practices, testing, monitoring, and observability; partner with product, data, engineering, and governance to deliver measurable outcomes.

Design, build, and maintain secure cloud-based frameworks to host and deploy ML models using Python and AWS. Collaborate with data scientists and platform teams to productionize models, apply MLOps/LLMOps practices, use AI-assisted development tools, ensure CI/CD, resiliency, and operational stability, and contribute to engineering communities and architecture/design artifacts.

Develop, onboard, and manage vendor and in-house machine learning models to mitigate fraud and credit risk. Own end-to-end model lifecycle including feature engineering, training, testing/benchmarking, deployment readiness, and ongoing monitoring. Perform independent assessments of vendor models, prepare governance and regulatory documentation, quantify business impact of performance shifts, and collaborate with cross-functional partners and external vendors.

Lead design, build, and deploy scalable generative AI and ML solutions from prototype to production. Define semantic modeling and ontology strategy, integrate semantics across data pipelines and APIs, enable ontology-driven agents and workflows, implement MLOps, monitoring, and responsible AI controls, and mentor engineers and data scientists to deliver measurable business outcomes.

Lead product strategy and lifecycle for AI infrastructure platforms, prioritizing scalability, resiliency, compliance, and customer value. Drive discovery, market research, and backlog management; define KPIs (cost, reliability, risk posture); partner with engineering and cross-functional teams to deliver GPU-enabled model training, inference, and workload scheduling solutions for enterprise clients.

Support end-to-end model development and production monitoring for finance decision optimization. Translate business needs into technical requirements, prototype solutions, perform data validation and testing, partner with quantitative and technology teams, and deliver SQL/Python-based analytics to drive measurable finance outcomes.

Design, prototype, and productionize LLM-based architectures for structured and unstructured financial data. Implement pre-training objectives, optimize training throughput, monitor and iterate on deployed models, mentor engineers, and collaborate with product teams to improve data quality, reduce resolution times, and increase consistency across operational workflows.

Lead design, code, and architect scalable hybrid-cloud solutions for transactional and analytics workloads. Implement and review high-quality code, CI/CD pipelines, and AI/GenAI systems. Collaborate with development leads and stakeholders, present technical updates, prioritize work, and mentor team members to ensure robust, production-grade delivery and monitoring.

Define and drive the multi-year fraud AI/ML platform roadmap spanning models, feature infrastructure, graph intelligence, and MLOps. Manage experimentation, productionization, feature ecosystems, and graph feature/embedding strategy. Partner cross-functionally to reduce fraud loss, ensure operational freshness, explainability, and communicate capabilities to leadership.