AI Platform Engineer - Associate

Morgan Stanley•Alpharetta, GA

About The Position

Our mission is to develop a firmwide Artificial Intelligence (AI) Development Platform that aligns with the firm's Technology principles and drives efficiency and consistency, controls, security and strong governance and promotes innovation, enabling teams to build applications that leverage AI capabilities and accelerate the adoption of AI across our businesses. This role is for a platform engineering specialist who will help build a firmwide AI Development Platform and drive adoption of AI capabilities throughout the enterprise. We have multiple focus areas across the platform and are looking for energetic, multi-disciplinary candidates who are eager to contribute to providing scalable, secure, enterprise-wide solutions for the firm. The ideal candidate will have strong hands-on experience building software platforms on any combination of the following platforms - Kubernetes, Cloud (AWS, Azure, and/or Google), API based development, REST framework, data engineering, and large-scale API Gateway environments etc. Knowledge of AIML and hands-on experience implementing solutions using Generative AI are also preferable. The candidate will have great communication skills, a team-based mentality and a strong passion for using AI to increase productivity as well as help generate new ideas for product & technical improvements. In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.

Requirements

Bachelor's or master's degree in computer science or related field, or equivalent job experience
5 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 FAST Api.
Broad understanding of data engineering (SQL, NoSQL, Big Data, Kafka, Redis), data governance, data privacy and security.
Experience in development, management, and deployment of Kubernetes workloads, preferably on OpenShift.
Experience with designing, developing, and managing RESTful services for large-scale enterprise solutions.
Experience deploying applications on Azure, AWS, and/or GCP using IaC (Terraform)
Hands-on experience with multiprocessing, multithreading, asynchronous I/O, 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 across organizations
Demonstrated experience in DevOps, understanding of CI/CD (Jenkins) and GitOps.
Knowledge of DevOps and Agile practices.

Nice To Haves

Practitioner of unit testing, performance testing and BDD/acceptance testing.
Understanding of OAuth 2.0 protocol for secure authorization.
Proficiency with Open Telemetry tools including Grafana, Loki, Prometheus, and Cortex.
Good knowledge of Microservice based architecture, industry standards, for both public and private cloud.
Good understanding of modern Application configuration techniques.
Hands on experience with Cloud Application Deployment patterns like Blue/Green.
Good understanding of State sharing between scalable cloud components (Kafka, dynamic distributed caching).
Good knowledge of various DB engines (SQL, Redis, Kafka, etc) for cloud app storage.
Experience building AI applications, preferably Generative AI and LLM based apps.
Deep understanding of AI agents, Agentic Orchestration, Multi-Agent Workflow Automation, along with hands-on experience in Agent Builder frameworks such Lang Chain and Lang Graph.
Experience working with Generative AI development, embeddings, fine tuning of Generative AI models.
Understanding of ModelOps/ ML Ops/ LLM Op.
Understanding of SRE techniques.

Responsibilities

Build and enhance Python-based microservices (Flask, Fast API) by empowering the AI platform.
Extend and maintain connectors for external LLM providers: OpenAI, Azure AI Foundry, and AWS Bedrock.
Build Helm charts, parameterized templates, and multi-env promotion workflows to develop modern containerized workloads.
Develop tooling and self-service capabilities for deploying AI solutions for the firm leveraging Kubernetes/OpenShift, Python, authentication solutions, APIs, REST framework, etc
Have a platform mindset and build common, reusable solutions to scale Generative AI use cases using pre-trained models as well as fine-tuned models.
Author best practices on the Generative AI ecosystem, when to use which tools, available models such as GPT, Llama, Hugging Face etc. and libraries such as Langchain.
Analyze, investigate, and implement GenAI solutions focusing on Agentic Orchestration and Agent Builder frameworks.
Author and publish architecture decision records to capture major design decisions and product selection for building Generative AI solutions. Inclusive of app authentication, service communication, state externalization, container layering strategy, and immutability.
Ensure AI platform is reliable, scalable, and operational; (e.g. blueprints for upgrade/release strategies (E.g. Blue/Green); logging/monitoring/metrics; automation of system management tasks)
Participate in all team's Agile/ Scrum ceremonies.
Participate in team's oncall rotation in build/run team model