Senior AI & Automation Quality Engineer - Senior Associate

Experienced Automation and AI/ML Engineer with a strong background in Quality Engineering, Data Analytics, and Generative AI solutions. Proven expertise in designing scalable automation ecosystems, building AI-powered testing frameworks, and delivering data-driven insights through advanced analytics and dashboarding.

Skilled in transforming raw data into actionable intelligence, driving intelligent test automation, predictive quality models, and autonomous testing systems for enterprise-scale applications.

Key Responsibilities

1. Automation Engineering Leadership

• Design, develop, and maintain scalable, enterprise-grade automation frameworks using Selenium and Playwright
• Build reusable automation components, libraries, and utilities for cross-project adoption.
• Develop automated test suites covering:
  • Functional
  • Regression
  • API
  • End-to-End scenarios
• Lead automation strategy, ensuring maximum coverage and framework standardization.
• Integrate automation into CI/CD pipelines (Jenkins, GitHub Actions, Azure DevOps)
• Perform strong database validation with advanced SQL queries.
• Ensure quality delivery through:
  • Test planning & execution.
  • Defect management
  • Reporting and metrics tracking
• Drive automation initiatives across programs and mentor junior engineers.

2. Generative AI & Agentic AI Engineering

• Design and implement AI-driven testing solutions leveraging LLMs and Generative AI
• Build RAG-based systems, AI copilots, and conversational test frameworks.
• Develop agentic workflows using LangChain & LangGraph for multi-step reasoning and orchestration.
• Implement:
  • AI-driven test case generation
  • Intelligent test data creation
  • Anomaly detection and defect prediction
• Integrate Hugging Face models and LLM APIs for custom QA use cases.
• Apply prompt engineering, fine-tuning, and model evaluation techniques.
• Drive innovation in autonomous testing systems and AI-led Quality Engineering

3. AI/ML & Data Engineering for Quality

• Build AI/ML models for defect prediction, test optimization, and quality forecasting.
• Design validation frameworks for data pipelines (Databricks, Snowflake, Azure)
• Develop analytics dashboards and KPI tracking solutions to improve decision making.
• Perform:
  • Data cleaning and transformation
  • Exploratory Data Analysis (EDA)
  • Feature engineering and model evaluation
• Deliver business insights through interactive dashboards and reporting solutions.
• Support both legacy and modern platforms with data-driven and AI-powered testing.

4. Engineering & Cross-Functional Collaboration

• Collaborating with Product Owners, Developers, DevOps, and Business Teams
• Translate requirements into automation frameworks and AI solutions.
• Participate in Agile ceremonies and provide leadership in test strategy.
• Conduct code reviews, governance, and enforce best practices.
• Evaluate and onboard new tools, frameworks, and emerging AI technologies.
• Drive continuous improvement and innovation initiatives.

Required Skills & Experience

Core Automation Expertise

• 8+ years in QA Automation / SDET / Quality Engineering
• Strong experience in:
  • Selenium WebDriver
  • Playwright (Java / Python)
• Expertise in framework design from scratch
• Hands-on experience in:
  • API Testing (REST / SOAP)
  • BDD frameworks (Cucumber, TestNG)
  • CI/CD integration
  • Agile/Scrum methodologies

Programming & Engineering Skills

• Proficiency in:
  • Python / Java / JavaScript / TypeScript
• Strong SQL expertise (mandatory)
• Solid foundation in:
  • OOP principles
  • Data structures & algorithms
  • Design patterns for automation frameworks.

GenAI & Agentic AI Capabilities

• Hands-on experience with:
  • LangChain / LangGraph
  • Hugging Face / Transformers
  • LLM APIs (OpenAI, etc.)
• Deep understanding of:
  • RAG architecture
  • Prompt engineering
  • AI agent orchestration
  • Vector databases (FAISS, Pinecone)

AI/ML & Data Analytics Skills

• Machine Learning:
  • Supervised & Unsupervised learning
  • Forecasting models
  • Model evaluation techniques.
• NLP & GenAI:
  • Transformers, embeddings, LLMs
• Data Analytics:
  • Data cleaning & EDA
  • KPI tracking & dashboard development.
  • Data visualization

Tools & Technologies

• Programming & Data:
  • Python…