Principal Data GENAI Platform Engineer - Senior Vice President
Citi
Chennai Corporation, Tamil Nadu, India
Full-Time
Competitive
RemoteExecutive
Summary
We are seeking a highly motivated and experienced Senior Vice President – Senior Lead, Python Engineering and Generative AI Platforms to lead our Retail and Wealth Risk Engineering organization within Enterprise Risk Technology. This is a senior leadership role responsible for defining strategy, architecture, and execution of enterprise-scale AI agent platforms, Python-based data ecosystems, and full-stack solutions.
The ideal candidate is a forward-thinking technology leader who can effectively manage Product based(PDLC) teams comprising ~15+ engineers, including AI-assisted/virtual developers (e.g., Devin.AI, GitHub Copilot). You will be instrumental in driving the adoption of AI Product Development Lifecycle (AI PDLC), regulatory-compliant data platforms, and scalable GenAI solutions, while aligning technology initiatives with Retail Risk, Wealth Risk, and regulatory programs such as CCAR (14Q/14A) and FDIC.
Responsibilities
Lead multiple agile scrum teams comprising ~15+ engineers, including hybrid teams of human engineers and AI-assisted development (Devin.AI, Copilot), ensuring delivery excellence and alignment with business priorities.
Define and execute the enterprise strategy for Python engineering, AI agent platforms, and full-stack data applications, aligned with Retail and Wealth Risk objectives.
Serve as the senior architect and technical authority for enterprise-scale AI agents, data engineering pipelines, and microservices-based applications, ensuring scalability, resilience, and security.
Drive the adoption and operationalization of AI Product Development Lifecycle (AI PDLC), including model governance, evaluation, deployment, monitoring, and compliance with Model Risk Management (MRM).
Lead development of high-volume data pipelines and data federation layers using PySpark, Databricks, Kafka, and Data Mesh architecture to support regulatory reporting (CCAR, FDIC) and risk analytics.
Architect and oversee GenAI agent ecosystems using LLMs (Google ADK, Gemini/Flash), implementing Human-in-the-Loop (HITL) frameworks to ensure explainability, auditability, and compliance.
Drive AI-augmented software development lifecycle, integrating tools such as Devin.AI, GitHub Copilot, and MCP platforms through advanced prompt engineering and governance guardrails.
Lead microservices and cloud-native architecture using FastAPI/Spring Boot, Kubernetes/OpenShift, and CI/CD pipelines, ensuring high availability and performance.
Drive engineering efficiency and standardization by reusing and repurposing enterprise-level frameworks, platforms, and tools, reducing duplication and accelerating delivery across teams.
Ensure all engineering solutions incorporate data governance and non-functional requirements, including Data Quality (DQ), data lineage, data tracing, and auditability, aligned with enterprise governance processes and regulatory expectations.
Act as a key partner to Risk, Finance, and Retail Banking stakeholders, translating regulatory and business requirements into scalable engineering solutions.
Build and manage strong relationships with senior business leaders, leading strategic discussions, requirement gathering, and cross-functional alignment.
Establish engineering standards, governance frameworks, and best practices across teams, ensuring consistency, quality, and reuse.
Mentor senior engineers and engineering managers, fostering a culture of innovation, accountability, and continuous improvement.
Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients, and assets by ensuring compliance with applicable laws, rules, and regulations, and escalating control issues with transparency.
Qualifications
Required:
12+ years of relevant experience in enterprise application development, data engineering, or AI platform engineering, with a strong track record of leadership in regulated environments.
8+ years of experience leading multi-team Agile organizations (20+ engineers), including managing distributed and hybrid AI-assisted teams.
Advanced expertise in Python, PySpark, and Databricks ecosystem for large-scale data processing and ELT/ETL pipelines.
Proven experience architecting and implementing enterprise AI/GenAI platforms, including agentic AI frameworks, LLM integrations, and prompt engineering.
Hands-on experience with AI-assisted development tools such as Devin.AI and GitHub Copilot and integrating them into engineering workflows.
Strong experience with microservices architecture, APIs, and cloud-native deployment (Kubernetes/OpenShift).
Strong experience with event-driven architectures and streaming platforms (Kafka).
Deep understanding of data architecture, data mesh, data federation, and regulatory data requirements.
Exceptional leadership, communication, stakeholder management, and decision-making capabilities.
Experience with cloud platforms (AWS, Azure, GCP, Databricks) and modern data ecosystems.
Familiarity with frontend technologies (React/Angular) for full-stack solution delivery.
Proven client relationship management experience, with the ability to engage and influence senior business stakeholders, lead strategic discussions, and drive consensus across business and technology teams.
Preferred:
Strong exposure to Retail lending/Credit Risk and Regulatory platforms, including CCAR (14Q/14A /14M), FDIC reporting, and enterprise risk aggregation.
Deep Core Systems Expertise in Retail Banking domains such as: Cards, Mortgages, Loans, Wealth Lending, Finance and Risk data platforms
Strong Business Domain Knowledge with experience working directly with Retail Banking and Risk organizations, including understanding of business processes, architecture, and infrastructure.
Experience with containerization technologies (Docker, Kubernetes) and DevSecOps practices.
Knowledge of AI Product Development Lifecycle (AI PDLC) and model governance frameworks.
Relevant industry certifications (e.g., AWS/Azure Data/AI, Kubernetes).
Education
Bachelor’s degree/University degree or equivalent experience.
Master’s degree is preferred.
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