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Generative Ai Finance Jobs (NOW HIRING)

AIG

Generative AI Product Owner

Atlanta, GA

Generative AI Product Owner Be part of something groundbreaking At AIG, we are making long-term ... other financial services. We provide world-class products and expertise to businesses and ...

Citi

Junior Generative AI Application Developer

Irving, TX · Hybrid

$64K - $83K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Engineer the future of global finance. At Citi, our Tech team doesn't just support finance--we are ... React or Angular, Apigee TypeScript, HTML5 Generative AI & AI Agents: Prompt Engineering, Workflow ...

Morgan-Stanley

NY · On-site

$155 - $215/hr

This is a Generative AI & Machine Learning Engineering position at the Vice President level, which ... Morgan Stanley is an industry leader in financial services, known for mobilizing capital to help ...

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All JobsGenerative Ai Finance Jobs

Generative Ai Finance information

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$47.5K

$98.8K

$206.5K

How much do generative ai finance jobs pay per year?

As of Aug 14, 2026, the average yearly pay for generative ai finance in the United States is $98,757.00, according to ZipRecruiter salary data. Most workers in this role earn between $57,500.00 and $134,500.00 per year, depending on experience, location, and employer.

What is generative AI finance?

Generative AI Finance refers to the use of advanced artificial intelligence models, such as generative adversarial networks (GANs) and large language models, in financial services and operations. These technologies are used for tasks like automating financial reporting, generating investment strategies, detecting fraud, and forecasting market trends. Generative AI can analyze vast amounts of financial data, create realistic simulations, and generate insights that can help financial institutions make better decisions. As the field grows, it is transforming how banks, investment firms, and other financial organizations approach data analysis, risk management, and customer service.

What are the key skills and qualifications needed to thrive as a generative AI finance professional, and why are they important?

To thrive as a Generative AI Finance professional, you need a strong background in finance, data analysis, and machine learning, often supported by degrees in finance, mathematics, computer science, or related fields. Familiarity with AI frameworks (such as TensorFlow or PyTorch), programming languages (like Python), and financial modeling tools is typically required, along with relevant certifications in finance or data science. Strong problem-solving, communication, and adaptability skills help professionals bridge the gap between technical teams and business stakeholders. These skills and qualities are crucial for designing innovative AI-driven financial solutions that are accurate, reliable, and aligned with business objectives.

How does a generative AI finance professional typically collaborate with cross-functional teams within an organization?

Generative AI Finance professionals frequently work alongside data scientists, software engineers, and financial analysts to develop and implement AI-driven models for forecasting, risk assessment, and process automation. Collaboration is key, as finance experts provide domain knowledge while technical teams handle the model development and deployment. Regular meetings, shared project management tools, and clear communication channels help ensure alignment on project goals and timelines. These professionals also often present findings and recommendations to stakeholders, bridging the gap between technical solutions and business needs.

What is the difference between Generative Ai Finance vs Data Analyst?

AspectGenerative Ai FinanceData Analyst
Required CredentialsDegree in Finance, Computer Science, or related fields; knowledge of AI and machine learningDegree in Statistics, Mathematics, or related fields; proficiency in data analysis tools
Work EnvironmentFinance firms, tech companies, AI startups; focus on AI-driven financial solutionsCorporate, consulting, or financial institutions; focus on data interpretation and reporting
Employer & Industry UsageFinancial institutions integrating AI for predictive modeling and automationOrganizations analyzing data to inform business decisions and strategies

Generative Ai Finance involves developing AI models to generate financial insights, automate tasks, and create synthetic data, often requiring expertise in AI and finance. Data Analysts focus on interpreting existing data to support decision-making. While both roles work with data, Generative Ai Finance emphasizes AI model creation, whereas Data Analysts focus on data interpretation and reporting.

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What cities are hiring for Generative Ai Finance jobs?

Cities with the most Generative Ai Finance job openings:

What states have the most Generative Ai Finance jobs?

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Generative Ai Finance jobs near you
Infographic showing various Generative Ai Finance job openings in the United States as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, and 4% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution, with an average salary of $98,757 per year, or $47.5 per hour.

Generative AI - Senior Associate

JPMorganChase

Manhattan, NY • On-site

Full-time

Re-posted 15 days ago

Job description

Job Summary:
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers and businesses. In this role as a Senior Associate, Generative AI Engineer, you will design, build, and support production generative AI products and APIs, collaborating with various engineering teams to ensure reliability and performance.
Responsibilities:
• Build and operate production generative artificial intelligence services and reusable backend application programming interfaces for firmwide use
• Combine enterprise data assets with large language and multimodal models to deliver high-quality user experiences
• Design scalable architectures with clear interfaces and separation of concerns to enable broader developer adoption
• Implement batch and real-time processing patterns to support high-throughput, low-latency use cases
• Collaborate with cloud engineering and site reliability engineering partners to deliver resilient, observable systems
• Translate research concepts into production-ready software through experimentation, evaluation, and iterative hardening
• Optimize system performance, scalability, and cost across inference, storage, and compute
• Define and track measurable outcomes, including objectives and key results aligned to business needs
• Ensure responsible artificial intelligence practices, controls, and governance are embedded into delivery and operations
• Troubleshoot production issues, drive root-cause analysis, and implement preventative improvements
Qualifications:
Required:
• PhD in a quantitative discipline such as Computer Science, Mathematics, or Statistics, or equivalent practical experience
• 3+ years of experience as an individual contributor in machine learning engineering or applied machine learning software engineering
• Demonstrated experience delivering production machine learning services in an enterprise environment, including being accountable for service health
• Strong fundamentals in statistics, optimization, and machine learning theory with applied depth in natural language processing and/or computer vision
• Hands-on experience building distributed, multi-threaded, and scalable systems (for example Ray, Horovod, or DeepSpeed)
• Strong software engineering fundamentals, including data structures, algorithms, and software development lifecycle best practices
• Experience designing and delivering service-oriented systems and application programming interfaces with scalability and performance requirements
• Ability to define success metrics and write clear objectives and key results aligned to business expectations
• Strong problem-framing skills to align machine learning solutions to business objectives and constraints
• Excellent communication skills with the ability to influence and build trust across technical and non-technical stakeholders
Preferred:
• Experience designing and implementing pipeline workflows using directed acyclic graph frameworks (for example Kubeflow, DVC, or Ray)
• Experience building batch and streaming microservices exposed via gRPC and/or GraphQL
• Demonstrable experience with parameter-efficient fine-tuning, quantization, and quantization-aware fine-tuning for large language models
• Experience with advanced prompting strategies such as chain-of-thought, tree-of-thought, or graph-of-thought approaches
• Experience with multimodal large language model use cases (text plus image, speech, or video)
• Experience partnering closely with cloud engineering and site reliability engineering teams on production readiness and operations
• Experience measuring and improving model quality using offline evaluation and production monitoring
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.

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