{"id":2221291,"url":"https://alion.io/job/zensar-technologies-aes-delivery-digital-test-automation-engineer-3","title":"AES - Delivery - Digital Test Automation Engineer","company":{"id":59576,"name":"Zensar Technologies","domain":"zensar.com","url":"https://alion.io/company/zensar-technologies","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":88,"open_postings":63,"ghost_share":0.159,"stale_share":0.063,"repost_share":0.413,"time_to_fill_p50_days":5,"computed_at":"2026-10-28T05:59:10Z"}},"role":"QA","role_family":"QA","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":12000,"max_usd":30000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":35},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Hallucination","optional":false},{"name":"Incident Management","optional":false},{"name":"Jira","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"MS SQL","optional":false},{"name":"Playwright","optional":false},{"name":"Postman","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Rest API","optional":false},{"name":"Selenium","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Swagger","optional":false},{"name":"Datadog","optional":true},{"name":"Grafana","optional":true},{"name":"Java","optional":true},{"name":"JMeter","optional":true},{"name":"k6","optional":true},{"name":"LLMOps","optional":true},{"name":"Prometheus","optional":true},{"name":"Python","optional":true},{"name":"Scala","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-08T10:16:43Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-28T12:44:27Z","board_verified":true,"closed_at":null,"days_open":20,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":20},"description":"You will own quality for data pipelines, microservices, APIs, GUIs, and LLM/ML integrations running on Databricks and Microsoft Azure. The role focuses on test strategy, test case design and execution, automation, ticket management, and agentic QA for LLM workflows. Minimum experience: 5 years\n Key Responsibilities\nTest Strategy and Planning\nDefine and maintain QA strategy for data, API, GUI, microservices, and LLM/ML features; prioritize test coverage based on risk and business impact.\nTest Case Design and Execution\nCreate clear, reusable test cases for functional, integration, regression, performance, and pricing scenarios; execute manual and automated tests and maintain test artifacts.\nData and Database Testing\nValidate ETL/ELT pipelines on Databricks and Delta Lake; write and run T-SQL validation queries; verify data lineage, completeness, and correctness.\nAPI Testing\nDesign and run contract, integration, and negative tests for RESTful APIs; use Postman, OpenAPI/Swagger, and automated API test suites.\nGUI Testing\nCreate and execute UI test plans for web applications; perform exploratory testing and maintain automated UI tests using Selenium, Playwright, or equivalent.\nMicroservices Validation\nTest service contracts, message flows, resilience, and backward compatibility across containerized microservices; validate observability and error handling.\nLLM and ML QA\nDesign evaluation metrics and test harnesses for ML models and LLM integrations; validate inference correctness, hallucination rates, latency, and cost tradeoffs.\nPrompt Engineering and Agentic QA\nCreate minimalistic, cost-aware prompts and prompt templates; implement agentic QA processes for autonomous agents including safety checks, guardrails, and evaluation pipelines.\nPricing and Business Logic Testing\nValidate pricing engines, discount rules, billing flows, and edge cases; create scenario matrices and automated checks for pricing correctness.\nAutomation and CI/CD\nIntegrate tests into CI/CD pipelines; author automated test suites, test runners, and validation scripts; collaborate with SRE/DevOps on test environments.\nTicketing and Incident Management\nRaise, triage, and manage tickets in Azure DevOps or Jira; document reproduction steps, root cause analysis, and remediation.\nDocumentation and Collaboration\nProduce test plans, runbooks, release checklists, and postmortems; mentor junior QA engineers and participate in cross-functional reviews.\nRequired Skills and Experience\nExperience: Minimum 5 years in QA, software testing, or related roles.\nDatabricks and Data Testing: Hands-on experience validating Databricks pipelines, Delta Lake, and Spark outputs.\nMicrosoft SQL Server: Medium proficiency in T-SQL for validation queries and database testing.\nAzure: Practical experience with Azure Data Factory, ADLS/Blob Storage, Azure DevOps, and Azure Functions.\nAPI Testing: Strong experience with REST APIs, Postman, OpenAPI/Swagger, and automated API testing.\nGUI Testing: Experience with Selenium, Playwright, or equivalent UI automation tools and exploratory testing.\nMicroservices: Familiarity testing containerized services, message queues, and service orchestration.\nLLM and ML Fundamentals: Understanding of ML lifecycle, model evaluation, prompt engineering basics, and LLM failure modes.\nPrompt Engineering: Ability to craft minimalistic prompts and maintain prompt libraries for reproducible tests.\nAgentic QA Awareness: Knowledge of agentic QA concepts, safety checks, and evaluation metrics for autonomous agents.\nTesting Tools and Automation: Experience with test frameworks, CI/CD integration, and test data management.\nTicketing Tools: Experience using Azure DevOps, Jira, or similar.\nSoft Skills: Strong analytical thinking, clear written communication, and cross-team collaboration.\nNice to Have\nCertifications: ISTQB, Azure Data Engineer, or Databricks certifications.\nProgramming: Python for test automation and data validation; familiarity with Scala or Java.\nObservability: Experience with Application Insights, Datadog, Prometheus, or Grafana.\nPerformance Testing: Experience with JMeter, k6, or similar tools.\nLLM Ops: Experience with prompt versioning, cost monitoring, and model evaluation frameworks.\nInfrastructure as Code: Familiarity with Terraform or ARM templates for test environment provisioning.\nInterview and Evaluation Guide\nTechnical screen: SQL validation problem and API testing scenarios.\nPractical exercise: Create test cases and automated checks for a sample Databricks ETL and an API that returns pricing.\nLLM prompt exercise: Provide minimal prompts for a given LLM task and design evaluation metrics for correctness and safety.\nSystem design: Design an agentic QA pipeline for autonomous agents including test harness, safety gates, and monitoring.\nBehavioral: Examples of ticket triage, incident postmortems, and cross-team collaboration.\nSuccess Criteria at 6 Months\nTest Coverage: Comprehensive, automated test suites for data pipelines, APIs, and core GUI flows.\nLLM Readiness: Reproducible prompt templates, evaluation metrics, and agentic QA checks integrated into CI.\nReliability: Reduced production incidents and faster mean time to resolution through clear runbooks and test automation.\nPricing Confidence: End-to-end validated pricing flows with scenario tests and regression guards.\nCollaboration: Trusted QA partner to engineering and product teams with documented processes and mentoring outcomes.\n Key Responsibilities\nTest Strategy and Planning\nDefine and maintain QA strategy for data, API, GUI, microservices, and LLM/ML features; prioritize test coverage based on risk and business impact.\nTest Case Design and Execution\nCreate clear, reusable test cases for functional, integration, regression, performance, and pricing scenarios; execute manual and automated tests and maintain test artifacts.\nData and Database Testing\nValidate ETL/ELT pipelines on Databricks and Delta Lake; write and run T-SQL validation queries; verify data lineage, completeness, and correctness.\nAPI Testing\nDesign and run contract, integration, and negative tests for RESTful APIs; use Postman, OpenAPI/Swagger, and automated API test suites.\nGUI Testing\nCreate and execute UI test plans for web applications; perform exploratory testing and maintain automated UI tests using Selenium, Playwright, or equivalent.\nMicroservices Validation\nTest service contracts, message flows, resilience, and backward compatibility across containerized microservices; validate observability and error handling.\nLLM and ML QA\nDesign evaluation metrics and test harnesses for ML models and LLM integrations; validate inference correctness, hallucination rates, latency, and cost tradeoffs.\nPrompt Engineering and Agentic QA\nCreate minimalistic, cost-aware prompts and prompt templates; implement agentic QA processes for autonomous agents including safety checks, guardrails, and evaluation pipelines.\nPricing and Business Logic Testing\nValidate pricing engines, discount rules, billing flows, and edge cases; create scenario matrices and automated checks for pricing correctness.\nAutomation and CI/CD\nIntegrate tests into CI/CD pipelines; author automated test suites, test runners, and validation scripts; collaborate with SRE/DevOps on test environments.\nTicketing and Incident Management\nRaise, triage, and manage tickets in Azure DevOps or Jira; document reproduction steps, root cause analysis, and remediation.\nDocumentation and Collaboration\nProduce test plans, runbooks, release checklists, and postmortems; mentor junior QA engineers and participate in cross-functional reviews.\nRequired Skills and Experience\nExperience: Minimum 5 years in QA, software testing, or related roles.\nDatabricks and Data Testing: Hands-on experience validating Databricks pipelines, Delta Lake, and Spark outputs.\nMicrosoft SQL Server: Medium proficiency in T-SQL for validation queries and database testing.\nAzure: Practical experience with Azure Data Factory, ADLS/Blob Storage, Azure DevOps, and Azure Functions.\nAPI Testing: Strong experience with REST APIs, Postman, OpenAPI/Swagger, and automated API testing.\nGUI Testing: Experience with Selenium, Playwright, or equivalent UI automation tools and exploratory testing.\nMicroservices: Familiarity testing containerized services, message queues, and service orchestration.\nLLM and ML Fundamentals: Understanding of ML lifecycle, model evaluation, prompt engineering basics, and LLM failure modes.\nPrompt Engineering: Ability to craft minimalistic prompts and maintain prompt libraries for reproducible tests.\nAgentic QA Awareness: Knowledge of agentic QA concepts, safety checks, and evaluation metrics for autonomous agents.\nTesting Tools and Automation: Experience with test frameworks, CI/CD integration, and test data management.\nTicketing Tools: Experience using Azure DevOps, Jira, or similar.\nSoft Skills: Strong analytical thinking, clear written communication, and cross-team collaboration.\nNice to Have\nCertifications: ISTQB, Azure Data Engineer, or Databricks certifications.\nProgramming: Python for test automation and data validation; familiarity with Scala or Java.\nObservability: Experience with Application Insights, Datadog, Prometheus, or Grafana.\nPerformance Testing: Experience with JMeter, k6, or similar tools.\nLLM Ops: Experience with prompt versioning, cost monitoring, and model evaluation frameworks.\nInfrastructure as Code: Familiarity with Terraform or ARM templates for test environment provisioning.\nInterview and Evaluation Guide\nTechnical screen: SQL validation problem and API testing scenarios.\nPractical exercise: Create test cases and automated checks for a sample Databricks ETL and an API that returns pricing.\nLLM prompt exercise: Provide minimal prompts for a given LLM task and design evaluation metrics for correctness and safety.\nSystem design: Design an agentic QA pipeline for autonomous agents including test harness, safety gates, and monitoring.\nBehavioral: Examples of ticket triage, incident postmortems, and cross-team collaboration.\nSuccess Criteria at 6 Months\nTest Coverage: Comprehensive, automated test suites for data pipelines, APIs, and core GUI flows.\nLLM Readiness: Reproducible prompt templates, evaluation metrics, and agentic QA checks integrated into CI.\nReliability: Reduced production incidents and faster mean time to resolution through clear runbooks and test automation.\nPricing Confidence: End-to-end validated pricing flows with scenario tests and regression guards.\nCollaboration: Trusted QA partner to engineering and product teams with documented processes and mentoring outcomes.","description_format":"text","description_chars":10626,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","IT Outsourcing & Dedicated 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