Top AI & Machine Learning Jobs

Validate NLP and Generative AI models within Model Risk Management, assessing methodology, data integrity, performance, implementation, and monitoring. Develop test models, analytical reports, validation tools, and evaluation pipelines while managing model risk across the lifecycle. Collaborate with auditors, regulators, model stakeholders, and technology partners on quantitative modeling, governance, automation, and compliance initiatives.

Develop and deploy foundation models, generative AI, agentic systems, and traditional machine learning solutions at enterprise scale. Design multi-agent workflows, RAG applications, training and deployment pipelines, and human-in-the-loop systems. Pre-train, fine-tune, evaluate, monitor, and optimize transformer models using distributed computing and techniques such as distillation, quantization, pruning, LoRA, and PEFT. Collaborate with technical, business, regulatory, and audit stakeholders while supporting responsible AI, governance, risk management, and model performance improvement.

Manage and mentor quantitative analysts and credit risk analysts while leading hands-on development of foundation models, generative AI, agentic systems, LLM applications, and RAG solutions. Design, train, fine-tune, evaluate, deploy, monitor, and optimize transformer-based models and scalable AI infrastructure. Partner with business, technology, audit, and regulatory stakeholders on governance, risk management, model performance, and enterprise AI strategy.

Lead enterprise GenAI engineering strategy and deliver large-scale agentic AI solutions. Advise leadership, architect distributed systems, build LLM applications, orchestrate autonomous and multi-agent systems, and integrate retrieval, memory, prompting, and evaluation capabilities. Drive adoption, innovation, governance, and measurable business outcomes while collaborating across global teams.

Principal Engineer responsible for shaping enterprise GenAI strategy and delivering innovative, large-scale solutions. Advises leadership, leads complex technical challenges, and provides expertise across GenAI architecture, agentic AI, model evaluation, governance, guardrails, cybersecurity, compliance, and production deployment. Requires deep knowledge of LLMs, RAG, vector databases, LLMOps, multi-agent systems, AI safety, and enterprise risk management.

Leads advanced data science initiatives by analyzing large structured and unstructured datasets, developing statistical and machine learning models, and generating actionable business insights. Collaborates with stakeholders, communicates recommendations to nontechnical audiences, resolves complex analytical issues, mentors junior staff, and supports strategic decision-making across business domains.

Leads enterprise AI and analytics modernization for front-line monitoring, including Agentic AI, LLM, Python, GCP, SAS-to-Python migration, workflow automation, and risk signal detection. Defines AI architecture, governance, standards, and technology roadmaps while advising senior leadership and coordinating complex initiatives across business, risk, compliance, and technology teams. The role also drives enterprise adoption, platform transformation, stakeholder engagement, and team development.

Designs, develops, and deploys enterprise AI applications using LLMs, agentic frameworks, RAG, fine-tuning, and multi-agent systems. Leads scalable AI platform development, integrates models with enterprise products, establishes evaluation benchmarks, and ensures compliance with architecture, security, risk, governance, and responsible AI standards. Advises leadership, resolves complex technical challenges, develops reusable tools, and mentors engineers while collaborating across engineering, product, architecture, risk, and business teams.

Twelve-month rotational program for PhD-level quantitative analytics candidates. Participants develop and validate statistical, machine learning, and AI models for credit risk, financial crime, operational loss, customer experience, and portfolio forecasting. The role involves large-scale data analysis, model deployment, data engineering, collaboration with senior leaders, and risk-focused decision support. Successful participants transition into a full-time role after completing two six-month rotations.

Ten-week quantitative analytics internship applying statistical modeling, machine learning, artificial intelligence, and data analysis to financial risk challenges. Interns may forecast credit portfolio losses and revenue, develop credit scorecards, detect money laundering, predict operational losses, and validate quantitative models. The program includes mentorship, technical training, leadership exposure, and professional development in a collaborative environment.