Job Title:  T&T - EAD - Consultant - AI / ML Testing - Bengaluru - Engineering

Team Lead | Engineering, AI & Data - Engineering  I AI / ML Testing

Location:  Bengaluru.

The Team

Engineering helps Reimagine and re-engineer mission-critical operations and processes; Leverage engineering-led design, deep industry knowledge, and AI and data-driven insights to transform the technology platforms at the heart of business.

Working alongside team, we empower and drive mission-critical solutions whether we need to modernize existing systems or implement new technology products and platforms. Through innovation, we improve financial performance, accelerate new digital businesses and fuel growth. Learn more about Engineering, AI and Data

Your work profile

GenAI / Agentic AI Application Developer – Quality Engineering

Technical Key Responsibilities:

• Build GenAI/Agentic AI applications for QE use cases.
• Develop agentic workflows with orchestration + tools: planner-executor agents, tool routing, memory/context, retry/fallback, safe execution
• Build test automation copilots/agents that can:
• generate/update test scripts
• perform UI automation using Playwright/Selenium
• trigger and validate CI pipelines and results
• Create RAG pipelines for QE artifacts: requirements, stories, test cases, logs, defects, runbooks
• Implement integrations with QE/SDLC tools:
• Jira, Azure DevOps (ADO Boards/Test Plans/Pipelines), GitHub/GitLab
• Test management: TestRail / Zephyr / Xray
• CI/CD: Jenkins/GitHub Actions/Azure Pipelines
• Implement MCP-based tool integrations (Model Context Protocol) for enterprise tools and test systems
• Build secure services/APIs: prompt service, retrieval service, eval service; auth, RBAC, rate limiting
• Implement guardrails: PII masking, content filtering, prompt injection defense, secure tool execution boundaries
• Add observability: tracing, evaluation metrics, latency/cost monitoring; release regression gates

Required Skills

•       GenAI / LLM Application Development

•       Agentic AI / Multi-step orchestration

•       Python / TypeScript/Node.js (plus C# nice-to-have)

•       Backend/API engineering: REST APIs, microservices, auth/security

•       RAG architecture: embeddings, chunking, indexing, vector retrieval/reranking

•       Tool calling/function calling patterns

Testing / QE Toolchain (Strongly Preferred)

•       Playwright (UI automation); Selenium/Cypress (any)

•       Browser-use automation / UI navigation agents (browser control)

•       CI/CD integration: Azure Pipelines / Jenkins / GitHub Actions

•       Work mgmt integrations: Jira, Azure DevOps (ADO) APIs

•       Git integrations: GitHub/GitLab

•       Test mgmt: ADO Test Plans / TestRail / Zephyr / Xray

•       Reporting/quality dashboards integration

Agent / Workflow / Low-code Platforms (Preferred)

•       MCP (Model Context Protocol)

•       Dify (GenAI app platform)

•       n8n (workflow automation)

•       Lang frameworks: LangChain / LangGraph / LlamaIndex / Semantic Kernel

Vector DB / Search (any)

•       Pinecone / FAISS / Chroma / Weaviate / Milvus / Azure AI Search / Elastic (vector search)

Observability / Production Readiness

•       LangSmith, OpenTelemetry

•       Monitoring: Grafana/Datadog/Splunk/ELK

Platform

•       Docker, Kubernetes (nice)

•       Cloud: Azure/AWS/GCP

Key Skills :

GenAI Developer, LLM Application Development, Agentic AI, QE GenAI, Test Automation Copilot, Playwright, Selenium, Browser Automation (Browser-use), MCP (Model Context Protocol), RAG Pipelines, Embeddings, Vector Search, Tool Calling / Function Calling, Prompt Engineering, Guardrails, Prompt Injection Defense, Jira Integration, Azure DevOps (ADO) Integration, ADO Test Plans, CI/CD (Azure Pipelines/Jenkins/GitHub Actions), C#, Dify, n8n, LangChain, LangGraph, LlamaIndex, Microservices, REST APIs, Docker, Observability (LangSmith/OpenTelemetry)