AI/ ML Ops Engineer

Job Opportunity: AI/ML Ops Engineer Location: New York, NY (On-Site) About the Role We're hiring an AI/ML Ops Engineer to join the Analytics, Intelligence & Data Technology (AIDT) team within Morgan Stanley's Wealth Management Technology organization. You will work closely with Advanced Analytics, Machine Learning, and Platform teams across multiple geographies including India and New York to build and operationalize large-scale data pipelines, distributed systems, and production-grade ML workflows. You'll be part of the engineering roadmap for cloud adoption, scalable architectures, and automation practices - and work in a dynamic environment with minimal supervision while mentoring and collaborating with cross-functional teams. Key Responsibilities Design, implement, and operationalize distributed, scalable data pipelines (batch and real-time) Develop distributed applications supporting analytics, ML models, visualizations, rules, and web-applications Partner with Analytics and AIML teams to analyze features at scale and streamline operational workflows Contribute to metadata management, data modeling, and documentation Lead adoption of CI/CD, Data Ops, and ML Ops practices for analytics and AIML domains Build libraries/tools to ease development, monitoring, and operational control Serve as a SME to optimize team workflows and reduce time to market Required Skills & Experience Educational Requirements: Minimum B.E./B.Tech in Computer Science, Engineering, or related field Core Technical Skills: Strong hands-on experience with Python, advanced SQL, and shell scripting Expertise in data analytics and wrangling using Python / Spark / SQL Experience designing, architecting, and operationalizing data flows using Hadoop, Spark (Databricks or equivalent), and Snowflake Cloud & Big Data: Proven experience with Cloud platforms: Microsoft Azure (Databricks, Snowflake), AWS, and their ecosystem Development of large-scale distributed data-driven applications leveraging cloud technologies Datastore Knowledge: Practical experience with SQL & NoSQL technologies such as HDFS, S3, Snowflake, MongoDB, Splunk and in-memory stores Machine Learning Lifecycle: Understanding of applied ML lifecycle and MLOps - operationalizing ML models in production Pipeline Orchestration & Tools: Experience with orchestration, scheduling tools, monitoring, optimization, and workflow automation - familiar with CI/CD and Data Ops practices Soft Skills: Excellent written and verbal communication Ability to work in a fast-paced and dynamic environment Comfortable interacting with global teams and technical stakeholders