7 jobs in Celebal Technologies

Key Responsibilities

• Lead end-to-end delivery of large-scale Data Engineering, Data Science, and GenAI programs

• Manage enterprise data platforms built on Databricks, Spark, and cloud ecosystems (AWS/Azure/GCP)

• Drive implementation of MLOps/LLMOps pipelines including model deployment, monitoring, and governance

• Own program governance including planning, budgeting, resource allocation, and risk management

• Lead Agile delivery, sprint planning, and cross-functional team coordination

• Ensure scalability, reliability, and performance of data pipelines and AI solutions

• Build and mentor high-performing teams of Data Engineers, Data Scientists, and ML Engineers

Required Skills & Experience

• 10–15 years of experience in Project/Program Management with strong focus on Data & AI

• Proven experience managing large-scale Data Engineering and Data Science projects

• Strong hands-on exposure to Databricks, Spark, ETL pipelines, and modern data architectures

• Experience with cloud platforms such as AWS, Azure, or GCP Celebal Technologies

• Solid understanding of MLOps, LLMOps, and AI/ML lifecycle

• Experience working with GenAI, LLMs, RAG-based solutions, or AI-driven platforms

• Strong expertise in Agile/Scrum methodologies and SDLC execution

• Excellent stakeholder management and executive communication skills

Senior Frontend / React Native Engineer — Agentic SDLC / AI-Driven Development Lifecycle (AI-DLC)

Seniority: Senior / Principal (individual contributor, not a manager)

Experience: 10+ years of hands-on frontend / mobile engineering (mandatory)

MANDATORY: AGENTIC SDLC / AI-DLC ENTERPRISE EXPERIENCE

• Designed and implemented Agentic SDLC / AI-DLC workflows at enterprise scale in a frontend or mobile engineering context — covering AI-assisted code generation, automated design-to-code pipelines, agentic code review, and automated quality gates

• Integrated LLM-based agents or AI orchestration tools (AutoGen, CrewAI, LangChain, or equivalent) into production frontend/mobile engineering pipelines — not just proof-of-concept environments

• Implemented or consumed MCP (Model Context Protocol) servers to extend AI agent capabilities for frontend tooling, design system validation, or mobile delivery automation

• Built production-grade AI-assisted workflows using tools such as GitHub Copilot, Cursor, Claude Code, Windsurf, or similar — with demonstrated, measurable productivity and quality outcomes

• Worked hands-on with Figma MCP, design-to-code automation, or AI-powered design system enforcement at enterprise scale

• Applied prompt engineering, RAG pipelines, and context augmentation techniques specifically to frontend/mobile engineering tasks: component generation, test synthesis, accessibility validation, or documentation automation

• Demonstrated quantified outcomes from Agentic SDLC / AI-DLC adoption in frontend contexts — e.g., design-to-delivery cycle reduction, automated test coverage improvement, or defect escape rate reduction

• Experience governing AI tool usage in regulated enterprise environments, including data privacy requirements, secure context handling, and audit trail generation for AI-produced artefacts

KEY RESPONSIBILITIES

◗ Agentic SDLC / AI-DLC Frontend Platform

• Build and evolve the frontend engineering layer of client's Agentic SDLC / AI-DLC platform

• Develop AI-assisted workflows for Figma-to-React Native code generation, component synthesis, and design system compliance validation

• Integrate and extend MCP servers and AI agent tooling for frontend-specific SDLC automation: test generation, visual regression, accessibility checks, and code review

• Build RAG pipelines that ground AI agents in client's component library, design tokens, API contracts, and mobile engineering standards

• Establish AI-generated code quality gates that validate frontend artefacts against client's engineering standards before they enter the delivery pipeline

• Document reusable prompt templates, agent configurations, and agentic workflow blueprints for broader frontend team adoption

◗ Core Frontend & Mobile Engineering

• Develop and enhance production mobile and frontend applications using React Native, React, TypeScript, and JavaScript

• Work with Re.Pack for React Native bundling, module federation, micro frontend architecture, and scalable mobile application delivery — mandatory hands-on experience required

• Build reusable components aligned with design system standards; maintain and extend Storybook component libraries

• Own features end-to-end: from design handoff through implementation, testing, deployment, and monitoring

• Write comprehensive automated tests using Jest, React Native Testing Library, Playwright, Appium, Maestro, or equivalent tooling

◗ Engineering Collaboration

• Collaborate with design teams to define and improve Figma-to-React Native implementation workflows

• Collaborate with QA teams to improve test automation, regression testing, mobile testing, and visual validation workflows

• Participate actively in code reviews, architectural discussions, and agentic SDLC community of practice sessions

• Work with backend, DevOps, and security teams to deliver compliant, performant, and secure frontend systems

React Native & Frontend (Non-Negotiable)

• React Native — deep, production-level expertise: architecture, navigation (React Navigation), state management (Redux Toolkit, Zustand, Jotai), performance optimisation, native module bridging

• Re.Pack — hands-on experience with React Native bundling, module federation, and scalable micro frontend-style mobile architecture (mandatory)

• React, TypeScript, JavaScript — strong, production-grade proficiency

• Modern frontend tooling: npm/yarn/pnpm, Metro, ESLint, Prettier, Babel, Git

• Design systems and component libraries: building, maintaining, and consuming; Storybook

• Automated testing: Jest, React Native Testing Library, Cypress, Playwright, WebDriverIO, Appium, or Maestro

◗ Agentic & AI Tooling (Non-Negotiable)

• Production-level experience with AI-assisted development tools: GitHub Copilot, Cursor, Claude Code, Windsurf, or equivalent — with measurable outcomes, not just usage

• MCP (Model Context Protocol) server implementation or consumption for frontend tooling, design system integration, or mobile delivery automation

• AI agent orchestration for frontend SDLC tasks: component generation, test synthesis, visual validation, or documentation automation

• Prompt engineering for code generation, design-to-code transformation, and test synthesis at enterprise scale

• Figma MCP, Figma Dev Mode, or equivalent design-to-code automation tooling — hands-on implementation experience

• RAG pipeline experience for grounding AI agents in design system tokens, component specs, and mobile engineering standards

• Local/private LLM deployment (Ollama, vLLM, or similar) for secure, enterprise-compliant agentic workflows

◗ CI/CD, Monorepo & Mobile Delivery

• GitHub Actions CI/CD pipelines including AI-powered quality gates, automated visual regression, and agentic code analysis steps

• Mobile CI/CD: Fastlane, Bitrise, App Center, or equivalent; app distribution workflows

• Monorepo tooling: Nx, Turborepo, or equivalent

• Containerisation basics: Docker; understanding of microservice deployment for BFF/API layers

◗ Security, Compliance & Enterprise Context

• Secure coding practices for mobile: certificate pinning, secure storage (Keychain/Keystore), jailbreak/root detection

• Data privacy and PII minimisation in mobile applications operating in regulated environments

• AI governance in enterprise contexts: audit trails for AI-generated frontend artefacts, safe context handling for LLM prompts containing design/code data

• Accessibility standards: WCAG 2.1 AA, React Native Accessibility API

QUALIFICATIONS & EXPERIENCE

• 10+ years of professional hands-on frontend and/or mobile software engineering experience in production environments — mandatory

• Demonstrated, verifiable enterprise-scale Agentic SDLC / AI-DLC implementation experience in a frontend or mobile context — mandatory

• Deep, production-level React Native and Re.Pack experience — mandatory

• Experience in banking, fintech, or a similarly regulated, high-compliance domain — strongly preferred

• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field (or equivalent professional experience)

• Proven track record of delivering high-quality mobile and frontend systems at enterprise scale with measurable performance and productivity outcomes

NICE TO HAVE

• Experience with local LLM setup, evaluation, and private deployment for secure enterprise agentic development

• Understanding of RAG concepts: document ingestion, embeddings, vector databases, and context-aware generation applied to frontend engineering tasks

• Experience with visual testing, screenshot comparison, and UI regression testing at enterprise scale

• Experience building AI-powered developer tools, internal engineering assistants, or code analysis agents

• Knowledge of open banking, PSD2, or UAE CBUAE digital banking standards

• Contributions to open-source React Native, MCP server, or Agentic SDLC / AI-DLC tooling projects

Job Description: Senior Quality Engineer – Agentic AI & Autonomous Testing

Locations: Jaipur, Noida, Gurgaon, Pune, Bengaluru, Hyderabad

Duration: 3-6 Months Contract with possible extension

Experience: 10+ Years

Role Overview

We are seeking a Senior Quality Engineer (QE) with deep hands-on expertise in Agentic AI and Autonomous Testing systems to lead the next generation of quality engineering. This role goes beyond traditional automation to design, build, and operate intelligent QA agents capable of independently planning, executing, analyzing, and optimizing testing workflows across enterprise applications.

The ideal candidate will combine strong QE foundations, AI/ML understanding, and engineering rigor to build self-learning test ecosystems that improve coverage, reduce manual effort, and enable continuous quality at scale.

This role is critical for establishing an AI-first QE capability where agents act as

autonomous testers, quality guardians, and optimization engines embedded across the SDLC.

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Key Responsibilities

1. Agentic AI Testing Architecture & Development

• Design and build autonomous QA agents capable of:

• Test discovery, generation, execution, and maintenance

• Failure diagnosis and root cause analysis

• Self-healing and adaptive test strategies

• Develop agent architectures using LLMs, workflows, and orchestration layers (e.g., sense → decide → act → learn loop)

• Define agent goals, constraints, and reasoning logic to enable independent decision-making in testing workflows

• Implement multi-agent ecosystems (test generation agents, validation agents, monitoring agents)

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2. Autonomous Test Strategy & Execution

• Build end-to-end autonomous testing frameworks that:

• Generate test cases from requirements, APIs, and production data

• Explore systems dynamically to uncover edge cases and untested paths

• Maintain and optimize test suites through continuous learning

• Design behavior-driven evaluation systems (not just assertion-based testing)

• Implement AI-driven regression, exploratory, and risk-based testing models

• Enable self-healing and adaptive execution to reduce maintenance overhead

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3. AI Validation, Evaluation & Observability

• Build evaluation frameworks for non-deterministic AI systems:

• Behavior-based validation (vs exact output matching)

• LLM-as-judge scoring frameworks

• Semantic and structured validation approaches

• Define and monitor AI-specific quality metrics:

• Accuracy, reliability, hallucination rates, drift, safety

• Implement continuous validation and observability pipelines

• Establish governance controls, auditability, and quality gates for AI-driven testing

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4. QE Platform Engineering & Integration

• Integrate agentic testing into CI/CD pipelines and DevOps workflows

• Build scalable AI-enabled automation frameworks across:

• Web, mobile, API, and backend systems

• Enable closed-loop learning systems where agents improve based on execution data

• Collaborate with engineering to embed quality as code / quality as platform

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5. AI-Driven Quality Transformation

• Drive transition from:

• Script-based automation → agent-based autonomous testing

• Manual validation → intelligent quality orchestration

• Define enterprise QE strategy for AI adoption (agent-first testing model)

• Introduce capabilities such as:

• AI-generated test assets

• Predictive defect detection

• Automated failure triage and clustering

• Act as SME for Agentic QA practices, tools, and frameworks

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6. Collaboration & Stakeholder Engagement

• Partner with:

• Engineering, Product, Data, Architecture, Business

• Translate business requirements into autonomous test strategies

• Drive cross-functional alignment on quality, risk, and governance

• Mentor teams on AI-driven QA practices and agent development

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Required Skills & Experience Core QE & Engineering

• 10–12+ years in Quality Engineering / Test Automation

• Deep expertise in:

• Automation frameworks (Selenium,WebdriverIO, Maestro, Cypress, etc.)

• API testing, performance testing, and integration validation

• Strong programming skills (Python, Java, JavaScript, or C#)

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Agentic AI & Autonomous Testing (Must-Have)

• Proven experience building or working with:

• AI testing agents / autonomous testing systems

• LLM-based workflows, prompt engineering, and reasoning systems

• Hands-on with:

• AI-driven test generation, self-healing frameworks, or adaptive testing

• Understanding of agent capabilities:

• Perception, reasoning, planning, execution, feedback loops

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AI/ML & Data Competency

• Working knowledge of:

• Machine learning concepts, NLP, embeddings, RAG

• Experience designing:

• Evaluation metrics and scoring systems for AI outputs

• Familiarity with:

• Data pipelines, model validation, and drift detection

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Modern QE & DevOps

• Experience integrating testing into:

• CI/CD pipelines (Jenkins, GitHub Actions, Azure DevOps)

• Strong understanding of:

• Microservices, APIs, distributed systems

• Exposure to:

• Cloud platforms (AWS, Azure, GCP)

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Advanced Skills (Highly Preferred)

• Experience with Copilot Studio / AI agent platforms

• Multi-agent system design and orchestration

• Experience in regulated environments (banking, fintech, compliance-heavy systems)

• Knowledge of Responsible AI / AI governance frameworks

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Behavioral & Leadership Competencies

• Strong systems thinking and problem-solving ability

• Ability to work in non-deterministic, probabilistic environments

• High ownership of quality, risk, and delivery outcomes

• Strong stakeholder communication (engineering + business)

• Ability to mentor and scale AI-first QE practices

About the Role:

As a Solutions Architect (SA) within Celebal Technologies team, you will engage with clients to address their big data challenges using the Databricks platform. You will be responsible for designing and delivering data engineering, data science, and cloud technology projects that integrate client systems, ensuring customers maximize the value from their data. Your role will also include providing training and handling technical tasks related to project completion. This is a billable position, and you will need to deliver excellent customer service while working closely with the regional Manager/Lead to meet project specifications

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Key Responsibilitie

s:1. Lead various impactful technical projects for customers, including designing reference architectures, developing technical "how-to" guides, and productionalizing customer use case

s.2. Work with engagement managers to scope and define the professional services work based on customer input and business need

s.3. Guide strategic customers through transformational big data and AI projects, as well as migrations from 3rd party platforms, delivering full end-to-end design, build, and deploymen

t.4. Provide expert consulting on architecture and design; help bootstrap or implement projects, driving the customer's understanding, evaluation, and adoption of Databrick

s.5. Deliver escalated support for operational issues faced by customer

s.6. Collaborate with Databricks' Technical, Project Manager, Architect, and Customer teams to ensure technical components of projects meet client requirement

s.7. Work closely with Engineering and Databricks Customer Support to provide feedback on product and resolve any product or support issues that arise during engagement

s.
Qualificatio

ns:1. 6+ years of experience in data engineering, data platforms, and analyti

cs.2. Proficiency in programming languages such as Python or Sca

la.3. Strong working knowledge of cloud ecosystems (AWS, Azure, GCP) with deep experti

se.4. Extensive experience with distributed computing using Apache Spark™ and a solid understanding of Spark runtime interna

ls.5. Familiarity with CI/CD practices for production deploymen

ts.6. Working knowledge of MLOps methodologi

es.7. Proven experience in designing and deploying performant, end-to-end data architectur

es.8. Experience managing technical project delivery, including scope and timelin

es.9. Excellent documentation, whiteboarding, and communication skil

ls.10. Proven ability to handle client engagements and resolve conflic

ts.11. Experience developing technical skills to support the deployment and integration of Databricks-based solutio

ns.12. Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent work e

Key Responsibilities

• Lead end-to-end delivery of large-scale Data Engineering, Data Science, and GenAI programs

• Manage enterprise data platforms built on Databricks, Spark, and cloud ecosystems (AWS/Azure/GCP)

• Drive implementation of MLOps/LLMOps pipelines including model deployment, monitoring, and governance

• Own program governance including planning, budgeting, resource allocation, and risk management

• Lead Agile delivery, sprint planning, and cross-functional team coordination

• Ensure scalability, reliability, and performance of data pipelines and AI solutions

• Build and mentor high-performing teams of Data Engineers, Data Scientists, and ML Engineers

Required Skills & Experience

• 10–15 years of experience in Project/Program Management with strong focus on Data & AI

• Proven experience managing large-scale Data Engineering and Data Science projects

• Strong hands-on exposure to Databricks, Spark, ETL pipelines, and modern data architectures

• Experience with cloud platforms such as AWS, Azure, or GCP Celebal Technologies

• Solid understanding of MLOps, LLMOps, and AI/ML lifecycle

• Experience working with GenAI, LLMs, RAG-based solutions, or AI-driven platforms

• Strong expertise in Agile/Scrum methodologies and SDLC execution

• Excellent stakeholder management and executive communication skills