Serve as an AVP-level technical analyst/developer within the Agility & Strategic Intelligence team supporting Morgan Stanley at Work, with primary focus on Generative AI enablement, AI platform adoption, and related data/integration tooling.
Partner with business stakeholders to gather, analyze, and document requirements, translating them into high-quality functional and technical specifications, user stories, process flows, data mappings, controls, and test scenarios.
Support the delivery of reusable AI capabilities and platform-aligned solutions that accelerate high-value GenAI use cases across the firm.
Design and build technical solutions based on requirements, including APIs/services, data transformation, workflow improvements, retrieval patterns, and automation enablement where appropriate.
Support and enhance existing tool-based workflows and AI-enabled solutions, including troubleshooting, coordinating fixes across teams and vendors, and strengthening data quality and control outcomes.
Bachelor’s or Master’s degree in Computer Science, Software Engineering, Business, Finance, Sciences, or equivalent education, training, and experience.
6+ years of progressive experience in software development, data integration, technical analysis, and/or solution delivery, with proficiency in multiple programming languages and a strong preferred focus on Python.
3+ years of technical leadership experience with the ability to lead, mentor, and coordinate developers/analysts to deliver high-quality solutions.
Strong background in requirements gathering and analysis, with the ability to produce functional requirements, user stories, acceptance criteria, technical specifications, data mapping, controls, and interface/ETL requirements.
Ability to translate ambiguous business needs into clear deliverables and executable technical plans for developers, testers, and partner teams.
Ability to balance functional analysis and hands-on technical execution; able to move from requirement discovery to build and delivery.
Proficiency in at least one mainstream programming language such as Python, Java, or C++; Python preferred.
Experience building or supporting AI applications, preferably Generative AI and LLM-based applications, including practical familiarity with AI APIs, embeddings, prompt/strategy management, and AI-driven automation.
Experience with data integration/ETL-style solutions, including data transformations, validations, reconciliations, and batch processing.
Solid understanding of database concepts, including data modeling, ER concepts, relational design fundamentals, and hands-on SQL, including complex queries with joins; experience with Oracle and/or MS SQL Server preferred.
Working knowledge of DevOps practices and tools, including CI/CD pipelines and version control systems such as Git/Bitbucket.
Understanding data security, data privacy, and compliance requirements in software development.
Experience with production support practices, including incident triage, root cause analysis, preventive fixes, and post-implementation validation.
Operational knowledge of Jira, Confluence, and Salesforce.
Experience in the financial services industry, with preference for exposure to Stock Plan Administration or equity administration concepts and platforms.
Excellent communication skills and demonstrated ability to partner effectively across technical teams, non-technical stakeholders, leadership, and internal partners.
Hands-on experience with SIP/SEACS (REF Computer), or comparable ETL/data integration tools such as Informatica or SAP Data Services, is a plus.
Hands-on experience with Kubernetes/containerized workloads and Flask/FastAPI services.
Experience with vector databases, enterprise search, semantic retrieval, and RAG patterns.
Hands-on experience with agent builder frameworks such as LangChain and LangGraph.
Ability to build and review APIs/services and Model Context Protocol (MCP) tool adapters.
Practitioner of unit testing, performance testing, and BDD/acceptance testing.
Understanding of OAuth 2.0 protocol for secure authorization.
Experience with knowledge graphs and/or entity resolution to improve retrieval grounding.
Proficiency with OpenTelemetry tools, including Grafana, Loki, Prometheus, and Cortex.
Understanding of LLMOps and DevOps practices, including CI/CD pipelines.
Experience with analytics/reporting platforms including Dataiku, plus Tableau/Power BI or equivalent tools.
Certified Scrum Master (CSM) or similar Agile certification.
Experience with data mining and cloud infrastructure such as Azure or Snowflake.
Exposure to automation/RPA concepts such as UiPath where it supports operational efficiency.
