FSRC-R and CT-RCT-PYTHON-AML-KYC-FRAUD-MANAGER

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EY GDS – RCT

Manager – Data, Cloud & AI Engineering

Manager – Technology Consulting

Experience: Strictly 8+ years

Mandatory eligibility:

Candidates must have 8 or more years of relevant professional experience, including hands-on Python, advanced SQL, cloud, AI, and Financial Crime Compliance domain experience. Candidates below 8 years or without any mandatory capability are not eligible.

The opportunity

Lead the delivery of data, cloud, and AI-enabled Financial Crime solutions for global financial services clients. This Manager role combines technical depth with delivery leadership, stakeholder management, team development, and end-to-end ownership. The successful candidate will shape solutions across KYC, AML, Transaction Monitoring, Sanctions, and Fraud while remaining close enough to the technology to guide architecture, review critical designs, resolve delivery risks, and assure quality.

Role overview

We are seeking an experienced Manager with strong Python and advanced SQL capability, practical cloud and AI delivery experience, and broad Financial Crime Compliance knowledge. The role owns outcomes from discovery and estimation through design, build, testing, deployment, transition, and production stabilization. It also includes team leadership, work planning, governance, financial oversight, client communication, and development of reusable assets and delivery standards.

Key responsibilities

• Lead end-to-end delivery of Financial Crime technology workstreams, programs, or managed-service outcomes. • Own solution scope, delivery plans, milestones, dependencies, risks, issues, quality, resource needs, and stakeholder commitments. • Provide technical direction for Python applications, advanced SQL solutions, cloud architecture, data pipelines, APIs, analytics, and AI components. • Translate KYC, AML, Transaction Monitoring, Sanctions, and Fraud requirements into scalable technical solutions and delivery roadmaps. • Partner with compliance SMEs, product owners, architects, engineering teams, data teams, model-risk stakeholders, QA, infrastructure, and operations. • Lead design reviews, code and SQL quality reviews, architecture decisions, defect triage, performance tuning, release readiness, and production stabilization. • Ensure appropriate security, privacy, auditability, explainability, data lineage, model governance, monitoring, and regulatory-control considerations. • Drive automation, reusable components, engineering standards, knowledge assets, and continuous improvement across the delivery lifecycle. • Prepare and present status, decisions, risks, dependencies, and recovery plans to senior client and internal stakeholders. • Support proposals, solutioning, effort estimation, staffing, capability development, and account growth activities.