This role is ideal for a technical leader who enjoys solving complex engineering problems, remains hands-on with technology, and can take systems from architecture and implementation through production deployment and operation. You will work closely with business and technology partners to deliver highly scalable, reliable, and intelligent products in a fast-paced investment banking environment.
What you’ll do in the role
Lead the end-to-end design, development, and delivery of scalable backend and Generative AI solutions from concept through production deployment.
Architect secure, resilient, high-performance distributed systems and services supporting enterprise applications and AI-powered capabilities.
Design and build backend services, APIs, data and orchestration layers, and platforms that can reliably support Generative AI applications at enterprise scale.
Provide hands-on technical leadership during architecture, solution design, implementation, code reviews, troubleshooting, and production support.
Drive technical decision-making to ensure solutions are scalable, maintainable, performant, and aligned with enterprise engineering standards.
Integrate Generative AI capabilities including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), intelligent agents, and related technologies into production applications where appropriate.
Collaborate closely with product owners, business stakeholders, architects, AI engineers, and engineering teams to translate business requirements into high-quality technical solutions.
Lead technical planning, estimation, execution, and delivery across multiple concurrent initiatives.
Ensure systems are production-ready with appropriate monitoring, observability, testing, security, resiliency, performance, and operational support.
Drive engineering best practices including CI/CD, automated testing, code quality, infrastructure automation, and, where applicable, MLOps.
Evaluate emerging technologies across Generative AI and software engineering and recommend practical adoption where they improve products, delivery, or engineering productivity.
Mentor engineers and promote engineering excellence through technical guidance, architecture and design reviews, code reviews, and knowledge sharing.
What you’ll bring to the role
10+ years of software engineering experience designing, developing, and delivering complex production-grade systems in enterprise environments.
Proven experience leading or collaborating with engineering teams and delivering complex technology initiatives in large enterprise environments.
Strong hands-on software engineering and system design skills, with demonstrated experience building scalable, resilient, and highly available production systems.
Strong programming skills in Python, Java, or another enterprise programming language, with the ability and willingness to work across technologies as needed.
Strong experience developing distributed systems using technologies and patterns such as microservices, REST APIs, event-driven architectures, containerization, caching, messaging, and cloud-native architectures.
Demonstrated experience taking complex systems into production, including scalability, resiliency, performance optimization, monitoring, observability, testing, security, and operational support.
Strong understanding of modern software engineering practices including CI/CD, automated testing, code quality, infrastructure automation, and DevOps.
Excellent communication skills with the ability to lead technical discussions and collaborate effectively across engineering, architecture, product, and business teams.
Experience working in Agile software development environments.
Experience with Generative AI/ML technologies or demonstrated interest and ability to develop expertise in building production-grade Generative AI systems.
Generative AI / AI Engineering Experience
Experience in one or more of the following is highly desirable, particularly for candidates coming from an AI engineering background:
Building production-grade Generative AI or machine learning applications.
Large Language Models (LLMs), prompt engineering, and model integration.
Retrieval-Augmented Generation (RAG), embeddings, vector databases, and enterprise search/retrieval architectures.
Agentic architectures, intelligent agents, tool/function calling, and workflow orchestration.
OpenAI, LangChain, LangGraph, or similar AI platforms and frameworks.
AI evaluation, monitoring, guardrails, and production observability.
MLOps, model serving, or AI platform engineering.
Building AI copilots, workflow automation, or agentic AI applications.
- Deep prior AI/ML expertise is not required for candidates who demonstrate exceptional backend, distributed systems, and production engineering experience together with the ability and interest to develop expertise in Generative AI.
- For candidates coming primarily from an AI engineering background, we are looking for demonstrated strength in software engineering, distributed systems, and operating applications in production, beyond prototyping and experimentation.
Preferred Qualifications
Experience within Investment Banking, Capital Markets, or Financial Services technology.
Experience with Kubernetes, Docker, GitHub Actions, Jenkins, or similar DevOps tooling.
Experience with MLflow or other MLOps platforms and practices.
Familiarity with cloud platforms such as Azure, AWS, or Google Cloud.
Experience designing high-throughput, low-latency, or highly available distributed systems.
Experience building platforms, frameworks, or shared services used by multiple engineering teams.
WHAT YOU CAN EXPECT FROM MORGAN STANLEY
At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work.
To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.
Expected base pay rates for the role will be between $155,000 and $215,000 per year at the commencement of employment. However, base pay if hired will be determined on an individualized basis and is only part of the total compensation package, which, depending on the position, may also include commission earnings, incentive compensation, discretionary bonuses, other short and long-term incentive packages, and other Morgan Stanley sponsored benefit programs.
Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.
Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.
For more information, please visit: https://www.morganstanley.com/people-opportunities/eeo.
