AI/ ML Ops Engineer
NYC, NY
Wealth Management Technology (WMIMT) is responsible for the design, development, delivery, and support of the technical platform behind the products and services used by the Business. Morgan Stanley Wealth Management (WM) is a product of the acquisition of Smith Barney from Citigroup, which was completed in June '13. Its core client base is individual investors, small- to medium-size businesses and institutions, and high net worth families and individuals. In the second half of '14, WM reached a milestone, with its business having surpassed $2 trillion in total client assets.
We're seeking someone to join our team as who can partner with the Advanced analytics, Machine learning and Platform team(s), across multiple project areas, and work in collaboration with team(s) in India & NY. The individual would be response for end-to-end development and operationalization of cross-system data flows, data stores and distributed applications for Analytics, AIML and Visualization along with stakeholder management. The person would also be part of the overall cloud adoption and engineering roadmap and ensure scalable, agile and robust architecture and implementation. Additionally, should be able to work in a dynamic environment with limited or no supervision and should be able to knowledge-share across other team members. Should be comfortable and manage time working with global team on multiple initiatives.
Analytics, Intelligence and Data Technology (AIDT) enables and drives strategic data initiatives and business capabilities across Wealth Management.
What you'll do in the role:
Design, Implement and Operationalize distributed, scalable, and reliable data flows that ingest, process, store, and access data at scale in batch / real-time
Develop distributed applications on-prem as well as on Cloud that scale to serve analytics, rules, web-applications, ML models and Visualizations for end-users
Partner with Analytics and AIML teams to develop and analyse features at scale. Provide SME level interface for team members to optimize their workflows, streamline operationalization and reduce time-to-market
Contribute to metadata management, Data modelling and documentation
Contribute to adoption of CI/CD, Data Ops and ML Ops practices within Data analytics, AIML and Visualization domains
Develop libraries to ease development, monitoring and control.
What you'll bring to the role:
Minimum B.E./B.Tech degree in Computer Science, Engineering, or a related field.
Experience working towards design, architecture, development, and operationalization of data flows across Hadoop eco-system, Spark (Databricks or otherwise), Snowflake and Cloud platform(s)
Understanding of applied Machine Learning (End-to-End) Lifecycle and Operationalizing AIML models in Production (MLOps)
Experience working on cloud platforms - Azure (Databricks, Snowflake), AWS, and their respective offerings
Experience in developing Large scale Distributed data-driven applications leveraging technologies defined above
Experience and understanding across key SQL and NoSQL datastores - HDFS, S3, Snowflake, MongoDB, Splunk as well as In-memory datastores
Proven understanding of the overall Data and Model deployment lifecycle and processing pipelines including orchestration, workflow scheduling tools, monitoring, optimization
Programming Languages - Expertise in Python, Advanced SQL, and Shell (Scripting),
Expertise in Data analytics and Data wrangling through complex and optimized Python / Spark / SQL
Ability to work in Fast paced and Dynamic environment.
GenAI Stack Langchain, RAG , Agentic Frameworks is always a plus
Good written and verbal communication skills
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