Top Artificial Intelligence Jobs

Designs and deploys production-grade generative and agentic AI systems for a regulated global bank. Responsibilities include context engineering, prompt engineering, RAG, knowledge graphs, Graph RAG, multi-agent orchestration, harness governance, MCP and A2A integration, observability, scalability, compliance, and responsible AI. The role partners with architects and business stakeholders, mentors engineers, conducts code reviews, and advances AI automation across Citi’s Controls Technology platform.

Lead the strategy, roadmap, and end-to-end delivery of an AI-first data platform for Investor Services. Drive productization of Funds and Custody data, applying AI and machine learning to ingestion, reconciliation, validation, anomaly detection, and forecasting. Define AI-powered client and developer tools, oversee launches and market adoption, and collaborate across Engineering, Data Science, Sales, and Marketing to deliver scalable cloud-based data products.

Leads the architecture, development, and production deployment of agentic AI solutions for Citi’s Client Network Services organization. The role combines hands-on full-stack and platform engineering with executive-level technical leadership, team development, stakeholder engagement, security, cloud infrastructure, Kubernetes operations, and platform migration. The leader owns technical strategy and delivery for Chorus, develops AI agents using ADK and MCP, drives engineering excellence and reliability, and partners with business users to deliver measurable outcomes.

Designs and develops enterprise generative and agentic AI applications using RAG, knowledge graphs, multi-agent orchestration, and context engineering. The role integrates foundation models, tools, and data through frameworks and interoperability protocols, while ensuring production scalability, observability, governance, security, and responsible AI compliance. Responsibilities include mentoring developers, reviewing code, optimizing streaming solutions, and collaborating with architects and cross-functional stakeholders.

Leads application development and systems analysis for generative AI solutions. Partners with management and technology teams to define enhancements, resolve complex problems, and ensure designs align with architecture standards. Develops, tests, debugs, and implements applications using Python, Java or C++, cloud platforms, APIs, machine learning frameworks, NLP tools, and data libraries. Advises developers and analysts while managing risk, compliance, and control requirements.

Leads application development and systems analysis initiatives focused on generative AI solutions. Partners with management and technology teams to define enhancements, resolve complex problems, and ensure architecture compliance. Develops, tests, deploys, and supports applications using modern programming languages, machine learning frameworks, APIs, cloud platforms, and containerization. Evaluates model performance, analyzes data, coaches developers and analysts, and manages technology risk, controls, and regulatory compliance.

Lead hands-on development, deployment, evaluation, and governance of enterprise Generative AI and LLM solutions in banking. Build automation, fine-tuning pipelines, semantic layers, RAG and agentic workflows, monitoring systems, guardrails, and adversarial testing frameworks. Partner with engineering, analytics, risk, compliance, legal, and business teams to deliver production AI solutions while supporting model documentation, regulatory compliance, and measurable business outcomes.

Lead the design, development, and deployment of an agentic generative AI platform for wholesale credit risk model development. Partner with quantitative analysts and model risk stakeholders to define use cases, architect secure RAG and orchestration capabilities, integrate data sources and LLMs, and establish CI/CD, testing, monitoring, and governance. Provide hands-on software development while leading, mentoring, and managing a team responsible for platform delivery.

Leads enterprise business intelligence strategy, product roadmap, semantic modeling, governance, delivery standards, and AI-enabled analytics. Partners with Data Engineering, Data Governance, Data Science, AI Solutions, IT, and business leaders to deliver trusted, scalable insights. Oversees BI adoption, performance, security, prioritization, and business value while leading, coaching, and developing the BI team and federated analytics contributors.

Design and develop production-grade multi-agent AI systems for financial pricing and hedging platforms. Responsibilities include building RAG pipelines, integrating LLMs and predictive models, implementing planning and reasoning systems, developing APIs and microservices, optimizing system performance, and ensuring resilient production operation. The role also provides technical leadership, subject matter expertise, mentoring, and strategic direction for enterprise AI initiatives.

Design, build, and deploy production Generative AI systems, LLM applications, and intelligent agents for enterprise workflow automation. Develop primarily in Python, implement RAG and transformer-based solutions, manage containerized deployments in OpenShift, and uphold testing, code review, documentation, and CI/CD practices. Explore emerging AI techniques, implement responsible AI guardrails, mentor others, and deliver measurable business value in an agile hybrid environment.

Lead the architecture and modernization of Citi’s cross-asset XVA valuation, risk, and stress-testing platforms. Drive distributed systems, cloud-native migration, Java and Python engineering, AI-enabled development, agentic workflows, MLOps, and legacy modernization. Partner with quants, trading desks, and risk teams to deliver scalable production systems while overseeing regulatory compliance, resiliency, governance, Agile execution, and globally distributed engineering teams.

Build and scale enterprise generative and agentic AI platforms for software delivery. Develop multi-agent workflows, RAG pipelines, evaluation harnesses, integrations with enterprise tools, and production microservices using Python and Java. Design resilient distributed systems, APIs, retrieval architectures, observability, CI/CD, containerized deployments, and secure guardrails. Collaborate with subject matter experts and frontend teams to deliver production-ready AI applications and forward-deployed solutions.

Leads the design, development, and deployment of scalable full-stack applications for M&A technology using Java, Angular, microservices, and event-driven architectures. Integrates agentic AI, GenAI, and LLM capabilities into regulated enterprise systems. Oversees software lifecycle activities, cloud and DevOps practices, security, testing, operational readiness, and risk. Mentors developers, conducts code reviews, partners with architecture and business stakeholders, and promotes modern engineering standards.

Lead governance and execution for an AI Product Development Lifecycle program. Coordinate Pod meetings, maintain objectives and controls, publish executive status dashboards, manage RAID logs, escalate risks, and drive mitigation. Engage regularly with MD-level and senior stakeholders, manage program resources, mentor team members, and ensure accountability across complex initiatives. Use enterprise project management tools to track milestones, dependencies, decisions, risks, and delivery progress.

Leads end-to-end program execution for Citi’s Applied AI and agent engineering initiatives. Responsibilities include cross-functional coordination, business-line intake and engagement, delivery milestone and SLA tracking, program dashboards, AI cost and ROI measurement, governance, model-risk reviews, security alignment, Agile delivery, dependency management, and blocker resolution. The role partners with engineering teams, enterprise infrastructure, business units, executive leaders, and risk-control functions to support compliant, scalable agent deployments.

Defines and governs Citi’s enterprise architecture for autonomous agents and multi-agent systems. The role establishes reference architectures, security and integration standards, evaluation practices, and governance review processes. It bridges business deployments with internal AI platforms, advises executive leadership, and leads architectural direction across engineering teams. Responsibilities include hands-on production LLM application architecture, enterprise integration, risk management, observability, and technical governance.

Lead Citi’s enterprise agentic AI strategy, roadmap, platform implementation, governance, and industrialization across global services. Translate business use cases into scalable AI capabilities involving orchestration, multi-agent systems, retrieval, tools, workflow automation, and human oversight. Partner with Product, Technology, Data Science, Architecture, Engineering, Risk, Compliance, and business teams to deliver production-ready solutions, establish responsible AI controls, develop operating models, and measure adoption and business value.

Leads enterprise business intelligence strategy, roadmap, product delivery, semantic modeling, governance, adoption, and AI-assisted analytics. Partners with Data Engineering, Data Governance, Data Science, AI Solutions, and IT to deliver scalable reporting and decision-support capabilities. Manages and develops the BI team, prioritizes demand, establishes delivery standards, monitors business value, and remains hands-on in solution design and technical troubleshooting.

Develop and maintain full-stack applications with front-end and back-end components, AI-assisted coding, Oracle PL/SQL, RESTful APIs, testing, CI/CD deployment, and troubleshooting. Participate across the software development lifecycle, advise stakeholders, assess technology risks, support business users, establish operating standards, and coach junior analysts. Work independently as a subject matter expert within an Agile technology team.

Build, test, deploy, and maintain secure, scalable full-stack enterprise applications and prototypes. Develop front-end experiences, backend services, APIs, integrations, authentication, and data persistence. Use AI coding assistants and integrate generative AI, RAG, agents, and tool calling when appropriate. Operate cloud-native solutions with CI/CD, infrastructure as code, automated testing, observability, and security controls. Collaborate with product managers and architects, troubleshoot production systems, document solutions, and communicate technical risks and tradeoffs.

Leads reporting, business intelligence, analytics, data engineering, automation, visualization, governance, and AI enablement. Develops scalable cloud-based data solutions, dashboards, predictive models, and actionable insights for commercial and leadership teams. Oversees data quality, reporting optimization, workflow automation, and self-service analytics while managing and mentoring an analytics team. Partners with technology and business stakeholders on data platforms, forecasting, customer reporting, and strategic initiatives.

Leads AI application architecture and engineering for Citi Wealth Technology, combining technical leadership, mentorship, architectural governance, and hands-on development. Designs production AI/ML and LLM systems, distributed event-driven platforms, microservices, APIs, cloud-native containerized applications, and secure CI/CD pipelines. Partners with business and technology stakeholders on technical roadmaps while enforcing engineering, security, quality, and scalability standards across multiple squads.

Build, test, deploy, and operate secure, scalable full-stack enterprise applications. Lead technical delivery for a workstream or small team, mentor engineers, conduct code reviews, and guide architecture and design decisions. Develop cloud-native services with CI/CD, infrastructure as code, observability, and enterprise security controls. Integrate generative AI, RAG, agents, and tool calling, evaluating solution quality, cost, latency, and security while collaborating with product managers, architects, IT, and business stakeholders.

Leads enterprise business intelligence strategy, product roadmap, semantic modeling, reporting standards, adoption, and delivery. Partners with Data Engineering, Data Governance, IT, Data Science, and AI Solutions to develop scalable, governed, AI-enabled analytics experiences. Manages BI demand prioritization, stakeholder relationships, team development, technical oversight, and business value measurement. Requires strong Power BI expertise, strategic leadership, technical depth, and experience managing BI products across multiple functions.