Software Architect Electronic Records Archiving, Modernization, Cloud & AI Enablement - VP

Morgan Stanley•Edison, NJ

11h

About The Position

This role is a VP Software Architect responsible for building, modernizing, and operating enterprise‑scale data records archiving solutions that support Morgan Stanley’s regulatory, legal, and data lifecycle obligations. The VP will actively engineer and modernize platforms spanning Archive360 (A360), IBM CMOD and other internal tools (ERA, etc.), evolving legacy Perl/Java/Shell‑based ingestion, monitoring, entitlement, and disposition tooling into Python‑first, cloud‑ready, AI‑enabled solutions. A core expectation of this role is to continuously improve efficiency —reducing cost, latency, manual effort, and operational friction while increasing throughput, reliability, and auditability.

Requirements

12+ years of enterprise software engineering experience with strong modernization ownership.
Advanced, hands‑on expertise in Python for building production services and automation.
Proven experience modernizing records archiving or data lifecycle platforms.
Strong working knowledge of Perl, Shell, and Java for legacy assessment and migration.
Deep understanding of Unix/Linux, batch processing, and high‑volume file ingestion systems.
Experience operating in regulated, audit‑heavy environments.
Python (advanced): services, tooling, concurrency, packaging, dependency management
Testing: unit/integration/contract testing for ingestion and disposition flows
APIs, messaging, batch orchestration, idempotency and error handling
Archive ingestion, reconciliation, and retrieval patterns
Retention schedules, legal/tax holds, WORM compliance
Disposition workflows and evidence generation
Platforms: Archive360, ERA, IBM CMOD (or equivalent)
Containers, orchestration platforms
Infrastructure as Code
Observability, performance tuning, RCA participation
AI coding assistants and refactoring tools

Nice To Haves

Workflow automation and agent‑based tooling

Responsibilities

Build and modernize Python‑based services, tools, and automation for data records archiving platforms.
Re‑engineer legacy Perl/Shell/Java tooling used for: Archive ingestion and reconciliation, Drop‑zone hygiene and monitoring, Entitlements and access controls, Disposition, retention validation, and reporting.
Personally lead complex refactors, performance tuning, and production issue resolution.
Implement CI/CD pipelines, test automation, and secure SDLC practices for archive‑related systems.
Participate in incident response, RCA, and remediation for archive ingestion, retrieval, or compliance issues.
Modernize archival workflows across Archive360, ERA, and IBM CMOD platforms.
Drive migrations such as: ERA → Archive360 ingestion and metadata remediation, Legacy batch/script pipelines → Python‑based orchestration, File‑based/manual processes → resilient, observable services.
Ensure compliance with WORM, SEC 17a‑4, retention schedules, legal/tax holds, and disposition controls.
Own migration execution artifacts: cutover plans, rollback strategies, reconciliation evidence, and audit support.
Define Python‑first, performance‑aware architectures for archiving platforms.
Make design decisions that balance: Cost efficiency, Processing speed and scalability, Regulatory risk and auditability.
Produce concise architecture artifacts (C4, ADRs, ingestion/disposition flows) that directly support efficient delivery.
Review designs and implementations to ensure efficiency considerations are embedded early—not added later.
Design and implement cloud‑ready archive tooling (public/private/hybrid), including: Secure ingestion pipelines, Metadata processing and enrichment, Monitoring, alerting, and reporting.
Implement infrastructure‑as‑code and environment parity for archive platforms.
Embed security controls: IAM, encryption, key management, entitlement enforcement, and audit logging.
Apply AI tools to accelerate: Code refactoring and modernization of legacy archive tooling, Automated test generation for ingestion and disposition workflows, Documentation and runbook creation.
Explore AI‑assisted automation for archive operations (triage, anomaly detection, reconciliation support).
Ensure responsible AI usage with human oversight and compliance alignment.
Partner closely with Records Management, Legal, Compliance, Data Governance, and Platform teams.
Mentor engineers on Python best practices, archive domain patterns, and regulatory‑aware design.
Influence technical direction through execution quality and subject‑matter expertise.
Communicate risks, tradeoffs, and progress clearly to senior technology leadership.