Automation engineer with AI - Vice president

Citigroup; Pune District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Citigroup seeks an App Dev Tech Lead Analyst (Vice President - C13) to steer AI-enabled QA, modern automation, and scalable testing architectures in a complex enterprise environment. You will mentor teams, shape strategy, and drive delivery velocity with AI-driven testing practices.

The role requires advanced communication across engineering, product, architecture, and business groups, ensuring quality metrics and defect prevention across mission-critical platforms.

Qualifications

• 10-12+ years in IT QA engineering or test architecture.
• Proven GenAI experience, integrating AI coding/tools into testing workflows.
• Expertise in enterprise-scale test automation frameworks (UI/API/Microservices).

Responsibilities

• Lead end-to-end automation architecture across UI and API tests.
• Mentor junior to senior QA engineers and drive upskilling programs.
• Own quality across SDLC/STLC with CI/CD gate validations.
• Collaborate with engineering, product, architecture, and business teams.

Skills

GenAI integration

QA leadership

SDET expertise

CI/CD experience

Mentorship

Tools

Selenium

Playwright

REST Assured

JMeter

Job description

The App Dev Tech Lead Analyst (Vice President - C13) is a strategic engineering leader who stays abreast of developments within quality engineering and Generative AI technologies, contributing directly to the directional strategy and execution within our testing ecosystem. As a recognized technical authority, this role drives the modernization, scalability, and AI-enablement of enterprise-grade test automation architectures. This role requires advanced communication, technical mentorship, and diplomacy skills to guide, influence, and collaborate across engineering, product, architecture, and business teams. You will have a significant impact on accelerating delivery velocity, defect detection, and quality metrics across complex, mission-critical platforms through modern AI-driven QA practices.

Key Responsibilities

AI-Driven Automation & Architecture Leadership

• Lead Next-Gen Automation Frameworks: Architect and lead the end-to-end development of robust, scalable UI and API automation frameworks across multi-tier enterprise applications.
• AI-Enabled Test Engineering: Champion and operationalize AI-driven engineering tools-including Friday.AI , GitHub Copilot , Microsoft Copilot , and Visual AI testing solutions -to accelerate automated script creation, expand test coverage, identify defects earlier in the SDLC, and eliminate manual regression effort.
• Autonomous API & Backend Automation: Leverage Devin AI and modern autonomous AI agents to build, optimize, and scale automated API test suites, and performance verifications across microservices and cloud ecosystems.
• Mentorship & Skill Development : Act as a hands-on technical mentor to junior, mid-level, and senior QA engineers, fostering upskilling programs in API testing, AI-assisted tooling, and advanced framework architecture.
• Intelligent Test Case & Data Generation: Implement Friday.AI to automatically generate comprehensive functional test scenarios, edge-case matrices, and realistic, compliant synthetic test data pipelines.
• Visual AI & Cross-Platform Validation: Integrate Visual AI validation engines to streamline UI/UX consistency, layout validation, and cross-browser/cross-device test executions.

Quality Engineering & Technical Delivery

• Full Lifecycle Quality Ownership: Drive quality throughout the entire software development lifecycle (SDLC/STLC), integrating automated gates within modern CI/CD pipelines (e.g., TeamCity , Jenkins, Harness).
• Core Automation Mastery: Maintain hands-on expertise in Java/Python-based automation stacks (Selenium, Playwright, REST Assured, TestNG, Cucumber/BDD) with solid OOP design patterns.
• End-to-End Test Execution: Plan, coordinate, and govern Functional, Automation, Integration, Performance, and User Acceptance Testing (UAT) rollout strategies alongside technology project managers and business stakeholders.
• Root Cause & Defect Analysis: Perform deep-dive root cause analyses on defect trends, environmental failures, and production incidents, instituting automated prevention guardrails.
• Traceability & Governance: Enforce end-to-end traceability across user stories, automated test definitions, execution runs, and defect management systems (Jira, ALM/Quality Center).

Governance, Mentorship & Risk Management

• Quality Metrics & Observability: Define, capture, and report actionable quality engineering metrics (e.g., automation pass rates, defect leakage, AI acceleration ROI, code/test coverage).
• Mentorship & Upskilling: Act as a technical advisor and coach for junior and mid-level quality engineers, driving AI tool adoption and engineering excellence across distributed teams.
• Risk Assessment & Ethical Conduct: Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding the organization, its clients, and assets by driving compliance with applicable laws, rules, and regulations, adhering to policy, and escalating control issues transparently.

Qualifications & Experience

Recommended Qualifications

• 10-12+ years of progressive experience in IT QA Engineering, Software Engineering in Test (SDET), or Test Architecture.
• Proven Experience with GenAI : Hands-on experience integrating AI coding and tools ( Devin AI, Friday.AI, GitHub Copilot, Microsoft Copilot, Visual AI testing suites ) into day-to-day testing and automation workflows.
• Framework Architecture Expertise: Demonstrated track record designing and maintaining enterprise-scale test automation frameworks (UI, API, Microservices) from the ground up using Java, Python, Selenium, Playwright, TestNG, Cucumber, or REST Assured.
• Performance Testing : API Performance testing using JMeter/LoadRunner
• Test Data & Synthetic Data Management: Deep understanding of test data management, parameterized data provisioning, and AI-based dynamic synthetic data generation.
• CI/CD & DevOps Integration: Solid experience embedding automated test suites into CI/CD build/release pipelines with automated gate validations.