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Morgan Stanley

Manages investments and advises on financing

AI Platform Engineer / Architect

Platform EngineeringConfirmed live in the last 24 hours
Full-timeNew York, NY, USAIn-person

About the Job

Requirements

  • A bachelor's or master's degree in Computer Science or a related field, or equivalent job experience.
  • At least 8 years of experience in software engineering, design, and development.
  • Strong hands-on application development background in at least one prominent programming language, preferably Python Flask or FastAPI.
  • Broad understanding of data engineering, including SQL, NoSQL, Big Data, and Kafka and Redis, as well as data governance, data privacy, and security.
  • Experience developing, managing, and deploying Kubernetes workloads, preferably on OpenShift.
  • Experience designing, developing, and managing RESTful services for large-scale enterprise solutions.
  • Experience deploying applications on Azure, AWS, and/or Google Cloud Platform using infrastructure as code (Terraform).
  • Hands-on experience with multiprocessing, multithreading, asynchronous input/output, and performance profiling in at least one prominent programming language, preferably Python.
  • Ability to articulate technical concepts effectively to diverse audiences.
  • Excellent communication skills.
  • Demonstrated ability to work effectively and collaboratively in a global organization across time zones and organizations.
  • Demonstrated experience in DevOps and understanding of continuous integration/continuous delivery (Jenkins) and GitOps.
  • Knowledge of DevOps and Agile practices.

Responsibilities

  • Develop tooling and self-service capabilities for deploying AI solutions for the firm using Kubernetes/OpenShift, Python, authentication solutions, APIs, and REST frameworks.
  • Develop Terraform modules and cloud architecture to enable secure AI cloud service deployment and consumption at scale.
  • Build common, reusable platform solutions to scale Generative AI use cases using pre-trained and fine-tuned models.
  • Use Kubernetes/OpenShift to develop modern containerized workloads.
  • Integrate capabilities such as large-scale vector stores for embeddings.
  • Author best practices for the Generative AI ecosystem, including guidance on tool selection and available models such as GPT, Llama, and Hugging Face, and libraries such as LangChain.
  • Analyze, investigate, and implement Generative AI solutions focused on Agentic Orchestration and Agent Builder frameworks.
  • Author and publish architecture decision records capturing major design decisions and product selection for Generative AI solutions, including application authentication, service communication, state externalization, container layering strategy, and immutability.
  • Ensure the AI platform is reliable, scalable, and operational, including blueprints for upgrade/release strategies such as Blue/Green, logging, monitoring, metrics, and automation of system management tasks.
  • Participate in the team's Agile/Scrum ceremonies.
  • Participate in the team's on-call rotation in a build/run team model.

Desired Qualifications

  • Knowledge of AI/ML and hands-on experience implementing Generative AI solutions.
  • Experience practicing unit testing, performance testing, and behavior-driven development/acceptance testing.
  • Understanding of the OAuth 2.0 protocol for secure authorization.
  • Proficiency with OpenTelemetry tools including Grafana, Loki, Prometheus, and Cortex.
  • Good knowledge of microservice-based architecture and industry standards for public and private clouds.
  • Good understanding of modern application configuration techniques.
  • Hands-on experience with cloud application deployment patterns such as Blue/Green.
  • Good understanding of state sharing between scalable cloud components, including Kafka and dynamic distributed caching.
  • Good knowledge of database engines such as SQL, Redis, and Kafka for cloud application storage.
  • Experience building AI applications, preferably Generative AI and large language model-based applications.
  • Deep understanding of AI agents, Agentic Orchestration, and Multi-Agent Workflow Automation, with hands-on experience in Agent Builder frameworks such as LangChain and LangGraph.
  • Experience with Generative AI development, embeddings, and fine-tuning Generative AI models.
  • Understanding of ModelOps, MLOps, and LLMOps.
  • Understanding of site reliability engineering techniques.

About the Company

Morgan Stanley

Manages investments and advises on financing

Morgan Stanley advises companies and governments on financing and manages investments for institutions, individuals, and families. Its business combines investment banking and securities trading with wealth management and an asset-management division that invests across public and private markets. Bankers arrange stock and bond sales and advise on mergers, while advisors build client portfolios and fund managers invest money under agreed strategies. The firm earns advisory and management fees, trading revenue, commissions, and interest from lending and deposits. Morgan Stanley was founded as a partnership in 1935 and is headquartered in New York City.

Company size
10,001+
Company stage
IPO
Headquarters
New York City, New York
Founded
1935

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