AI Solutions Engineer - Vice President
About the job
- Bachelor's or Master’s degree in Computer Science, Software Engineering, or a related quantitative field.
- 9+ years of hands-on software engineering experience, with a proven track record of building and deploying robust applications, and significant experience integrating AI/ML models.
- Demonstrated experience building and deploying end-to-end applications that leverage LLMs and related frameworks. This includes experience with prompt engineering, API integration, and working with agentic frameworks.
- Strong proficiency in programming languages such as Python, Java, or Go, along with experience integrating with relevant AI/ML frameworks (e.g., TensorFlow, PyTorch).
- Proven ability to translate complex business requirements into well-defined, cloud-optimized application architectures, scalable data models (e.g., relational, NoSQL, graph), and technical specifications for AI-powered systems, and to subsequently implement and accelerate delivery of robust, production-ready systems based on these designs.
- Extensive experience with major cloud platforms (e.g., AWS, Azure, GCP), including cloud-native services (serverless, containerization, managed AI/ML platforms), and a strong command of DevOps/MLOps best practices for automated deployment, monitoring, lifecycle management, data pipeline orchestration, and cloud security standards.
- Excellent communication capabilities, with the ability to articulate complex technical concepts to both technical and non-technical stakeholders across all levels of the organization.
- Strong collaboration and interpersonal skills, with a passion for mentoring and enabling others.
- Proven ability to lead or significantly contribute to cross-functional projects.
- Rapid Prototyping & Application Development: Lead the end-to-end development of applications that integrate and leverage AI/ML models, from architectural design, data schema design, data pipeline construction, and rapid prototyping to initial deployment and operationalization, utilizing cloud-native services (e.g., serverless, containerization, managed AI/ML platforms) and CI/CD pipelines for accelerated delivery. Implement robust MLOps practices to streamline model deployment, monitoring, and lifecycle management in cloud environments, including data versioning, feature store integration, and data pipeline management.
- Business Partnership & Solution Architecture: Collaborate closely with business and engineering teams to deeply understand their challenges and customer needs, identify high-impact opportunities to integrate AI capabilities into applications, and translate business requirements into robust cloud-optimized application architectures, scalable data models, and technical specifications for AI-powered solutions, considering scalability, cost-efficiency, security, and data governance principles.
- Solution Implementation & Delivery: Architect, implement, and deliver scalable, robust, and maintainable cloud-native AI applications that consume and operationalize AI solutions based on defined technical specifications and architectures, ensuring seamless integration with existing systems and workflows within the Goldman Sachs ecosystem. Apply strong software engineering principles, data modeling best practices (e.g., relational, NoSQL, graph), DevOps/MLOps best practices, and cloud security standards. Drive automation of deployment, testing, and monitoring processes to ensure rapid and reliable delivery of AI applications.
- Knowledge Transfer & Enablement: Facilitate effective knowledge transfer through comprehensive documentation, training sessions, mentorship, and pair-programming, empowering receiving teams to take ownership and continue the development and maintenance of AI-powered applications.
- Technology & Innovation Leadership: Stay abreast of the latest advancements in application development, system integration, AI/ML technologies, data management platforms, and operational best practices, continuously evaluating and recommending new tools, techniques, and architectural patterns to drive innovation in AI application delivery.
- Productionize LLMs: Build evaluation framework for open-source and foundational LLMs; implement retrieval pipelines, prompt synthesis, response validation, and self-correction loops tailored to production operations
- Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability
- Collaborate directly with users: Partner with production engineers, and application teams to translate production pain points into agentic AI roadmaps; define objective functions linked to reliability, risk reduction, and cost; and deliver auditable, business-aligned outcomes
- Scale and performance: Optimize cost and latency via prompt engineering, context management, caching, model routing, and distillation; leverage batching, streaming, and parallel tool-calls to meet stringent SLOs under real-world load
- Build agentic AI systems: Design and implement tool-calling agents that combine retrieval, structured reasoning, and secure action execution (function calling, change orchestration, policy enforcement) following MCP protocol
- Integrate with runtime ecosystems: Connect agents to observability, incident management, and deployment systems to enable automated diagnostics, runbook execution, remediation, and post-incident summarization with full traceability
About the company
Goldman Sachs advises companies and governments on mergers, raises money for them by selling their stocks and bonds, and trades securities for big investors such as hedge funds and pension funds. It led Ford Motor's 1956 stock market debut, then the largest share sale in US history. Traders buy and sell stocks, bonds, currencies, and commodities for clients and lend them money, while bankers earn a fee on each deal they advise on or arrange. Its asset and wealth management arm invests for institutions and wealthy families and charges fees based on the money it oversees. The firm was founded in 1869, when it began brokering short-term IOUs for New York merchants, and it is based in New York City.
Company Size
10,001+
Company Stage
IPO
Headquarters
New York City, New York
Founded
1869
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Simplify's Take
What believers are saying
- Trading drives the boom: stock-trading revenue hit a record $7.42 billion in the June 2026 quarter, up 72%.
- Pay is climbing: compensation rose 20% to $11.5 billion in the first half of 2026, while staff grew 1%.
- A Goldman stint opens doors: alumni Rishi Sunak, Mario Draghi and Mark Carney rose to lead Britain, Italy and Canada.
What critics are saying
- AI is costing jobs: Goldman's October 2025 AI plan came with a "limited reduction in roles" and slower hiring.
- Expect five office days a week: Goldman reaffirmed the rule in 2023, and its CEO calls remote work an aberration.
- Stock awards have lost value: shares fell 23% from July 15 to October 8, 2026, versus 18% at Morgan Stanley.
What makes Goldman Sachs unique
- Traders outearn Goldman's famous merger advisers: trading made 57% of first-half 2026 revenue, versus 8% from advice.
- Goldman is Wall Street's top merger adviser: it earned $4.6 billion advising on 2025 deals, versus JPMorgan's $3.1 billion.
- The top prize is still partner: though public since 1999, Goldman named just 95 partners from 46,000-plus staff in 2024.
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Benefits
Health Insurance
Dental Insurance
Vision Insurance
Life Insurance
Disability Insurance
Health Savings Account/Flexible Spending Account
Paid Vacation
Paid Sick Leave
Paid Holidays
Professional Development Budget
Company News
Morgan Stanley (MS) shares fell nearly 3% on Wednesday, even after third-quarter revenue rose 12% to $20.3 billion from $18.2 billion a year earlier and beat forecasts. Goldman Sachs also beat estimates, yet it lost another 1.5%. CNBC host Jim Cramer blames worries about sticky inflation and climbing rates, not weak results. "It's not easy to own these bank stocks right now," he said. Institutional Securities revenue grew 18% to $10 billion, while fixed income fell 7%. Wealth management revenue rose 10%, and client assets reached a record $10.5 trillion. Cramer said a resolution to the war in Iran could cool inflation and give the Federal Reserve room to hold off on rate hikes.
All six major US bank stocks fell after third-quarter earnings, even as several banks posted strong results. CNBC's Jim Cramer said investors are worried about persistent inflation, rising interest rates, and shaky capital markets. Citigroup revenue grew 8%, its best third quarter in a decade, yet shares dropped 5.6%. Goldman Sachs beat estimates, with investment banking fees up 21%, but its stock finished lower. Bank of America missed expectations and faced a $1.2 billion regulatory penalty; shares fell roughly 5%. Cramer sees buying opportunities. His Charitable Trust upgraded Wells Fargo and Goldman Sachs to buy-equivalent ratings.
Momei Qu, head of venture at PSP Growth, says Goldman Sachs and JPMorgan are becoming artificial intelligence (AI) stocks. She told Bloomberg's The Close that banks are tied to the AI buildout through debt financing, potential initial public offerings (IPOs) and mergers, though earnings do not show it yet. Lending was a bright spot last quarter. JPMorgan, Citigroup, and Wells Fargo posted a record $56.5 billion in combined net interest income, and JPMorgan's $107 billion outlook, excluding markets, beat the $103 billion consensus. Qu said investors are split on AI valuations: one camp excuses thin margins and heavy spending, while the other wants proof of returns. Anthropic holds its pre-IPO investor day on Wednesday. Bloomberg Intelligence analyst Herman Chan expects the Anthropic IPO to lift Goldman Sachs and Morgan Stanley. He warned that slower AI spending could cut banks' fee revenue from data center bond deals.
Four of Wall Street's biggest banks reported third quarter dealmaking fees of $8.47 billion, up 15% from a year earlier but down 7.5% from a banner second quarter. Goldman Sachs, JPMorgan Chase, Citigroup, and Wells Fargo said activity remains strong despite market volatility and higher borrowing costs. JPMorgan chief financial officer Jeremy Barnum said deals were "delayed rather than canceled" after a slowdown in late September, when volatility widened the gap between what buyers and sellers would accept. Goldman's fees rose 22% to $3.2 billion, led by record M&A advisory revenue. Wells Fargo's rose to $894 million, and Citigroup's rose 9% to $1.35 billion. Goldman chief executive officer David Solomon said some companies postponed initial public offerings (IPOs) in September rather than accept valuations 10% to 15% lower.
Goldman Sachs' equity trading revenue jumped 72% year over year to $6.44 billion in the third quarter, almost twice the $3.47 billion from fixed income. JPMorgan Chase's bond desk missed estimates by more than $500 million. JPMorgan's fixed income revenue fell 4% to $5.4 billion, its first decline in more than two years. Its equities desk rose 49% to $5.0 billion. Herman Chan, a senior US banks analyst at Bloomberg Intelligence, blamed expectations of prolonged high rates, which kept clients on the sidelines in September. "Goldman was the gold standard for the third quarter," he said. Higher rates still helped lenders. JPMorgan's net interest income rose 12.8% to $27.04 billion.