Top AI & Machine Learning Jobs

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.

Participate in a 12-month quantitative analytics development program with two rotations. Develop, evaluate, validate, and deploy statistical, machine learning, and AI models supporting credit risk, financial crime, operational risk, customer experience, and business decisioning. Analyze large datasets, build scalable solutions, collaborate with quantitative professionals and senior leaders, and translate analytical findings into actionable business insights.

Lead AI architecture and engineering for enterprise agentic AI systems. Define standards for RAG, agents, orchestration, evaluation, security, and governance; provide hands-on technical leadership, mentor engineers, and align cross-functional stakeholders to deliver secure, compliant AI solutions.

Build full-stack AI-enabled applications and backend services integrating MCP servers, LLMs, RAG retrieval, and agent orchestration. Design reusable MCP tools/skills, implement secure RBAC, ensure observability, and prepare deployments for OpenShift/Kubernetes while collaborating across product and platform teams.

PhD-level summer intern will apply advanced analytics, AI/ML, and quantitative techniques to develop, validate, and enhance pricing, simulation, and benchmark models for capital markets. Responsibilities include model development, performance monitoring, statistical validation, reporting metrics, and collaborating with quantitative teams to deliver scalable, risk-aware solutions over a 10-week program.

12-month rotational masters-level program applying advanced analytics, AI/ML, and quantitative methods to develop, validate, and enhance pricing and risk models for capital markets. Responsibilities include model development, simulation, benchmarking, monitoring production model performance, and producing analytical reporting to support trading desks and risk management.

12-month PhD development program rotating through quantitative analytics teams in capital markets. Work includes developing and validating pricing, risk, simulation, and benchmark models; monitoring and enhancing production model performance; applying statistical, numerical, and AI/ML techniques; and delivering scalable data-driven solutions with strong communication and risk awareness.