AI Ops Lead (Principal Tech Leadership & Strategy) (VP, P5)

Morgan Stanley•New York, NY

About The Position

Morgan Stanley is a leading global financial services firm providing a wide range of investment banking, securities, investment management and wealth management services. The Firm's employees serve clients worldwide including corporations, governments and individuals from more than 1,200 offices in 43 countries. As a market leader, the talent and passion of our people is critical to our success. Together, we share a common set of values rooted in integrity, excellence and strong team ethic. Morgan Stanley can provide a superior foundation for building a professional career a place for people to learn, achieve and grow. A philosophy that balances personal lifestyles, perspectives and needs is an important part of our culture. Position Summary The AI Ops Lead is a senior technology leader responsible for setting AI Ops direction and delivering solutions hands on within the Enterprise Network Services division. The role combines strategic leadership with active technical contribution, remaining closely engaged in solution design, development, and operational analytics while also establishing standards, priorities, and execution discipline across teams. Sitting at the intersection of AI/ML, data platforms, and technology operations, this role embeds intelligence into operational workflows to drive measurable improvements in resilience, efficiency, and insight. Delivery is performed within an Agile development framework, partnering with engineering, operations, and product stakeholders to move capabilities from concept to production.

Requirements

Demonstrated success leading complex initiatives while staying directly involved in technical delivery (design, code, reviews, troubleshooting, and analytics).
Advanced proficiency in Python with the ability to produce and guide production quality code for analytics, automation, and AI/ML solutions.
Strong expertise in SQL and modern data platforms, including Snowflake, supporting analytics, feature engineering, and operational reporting.
Solid understanding of AI/ML production patterns, including deployment, monitoring, retraining, and lifecycle management.
Hands on familiarity with BI/visualization and observability tooling such as Grafana, Power BI, and Tableau.
Practical experience delivering in an Agile development framework, collaborating closely with product owners, scrum masters, and engineers.
Ability to influence architecture and delivery outcomes across teams through strong technical judgment, clear communication, and stakeholder management.
Experience operating in enterprise scale, controlled environments with an emphasis on quality, resilience, and risk awareness.

Nice To Haves

Experience with AIOps / operational analytics / observability practices and tooling.
Familiarity with data engineering patterns (ETL/ELT, streaming, data quality frameworks).
Exposure to MLOps practices supporting CI/CD, monitoring, and reproducibility.
Proven ability to take capabilities from POC - pilot - production within an Agile delivery model.

Responsibilities

Design and deliver AI enabled operational capabilities, contributing directly to implementation using Python, SQL, and Snowflake based data platforms.
Lead technical execution across analytics, automation, and AI/ML enablement-driving solutions from requirements through production rollout and iteration.
Build and operationalize end to end AI and analytics workflows spanning data ingestion, feature engineering, model deployment, monitoring, and continuous improvement.
Operate within Agile delivery, participating in sprint planning, backlog refinement, reviews, and retrospectives while guiding teams toward iterative, outcome based delivery.
Develop and promote operational insights and dashboards using Grafana, Power BI, and Tableau, enabling data driven operational decisions.
Translate operational problems into clear AI/analytics use cases and execution plans, partnering with product owners and stakeholders to prioritize effectively.
Establish standards and guardrails for production AI, including data quality, observability, model performance monitoring, explainability, and responsible AI practices.
Coach and uplift engineers and analysts through code reviews, technical mentoring, and engineering best practices, fostering strong ownership and delivery discipline.

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