{"id":3975218,"url":"https://alion.io/job/newfold-digital-software-development-engineer-in-test-ai","title":"Software Development Engineer in Test - AI","company":{"id":7300,"name":"Newfold Digital","domain":"newfold.com","url":"https://alion.io/company/newfold-digital","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"QA","role_family":"QA","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Context Engineering","optional":false},{"name":"CrewAI","optional":false},{"name":"Cypress","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"GitHub","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"GraphQL","optional":false},{"name":"Hallucination","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Multimodal AI","optional":false},{"name":"OpenAI","optional":false},{"name":"Pinecone","optional":false},{"name":"Playwright","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Qdrant","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Robot Framework","optional":false},{"name":"Scrum","optional":false},{"name":"Selenium","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Shift-Left Security","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Weaviate","optional":false},{"name":"Claude","optional":true},{"name":"Copilot","optional":true},{"name":"Cursor","optional":true},{"name":"Gemini","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Red Teaming","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"Reinforcement Learning","optional":true},{"name":"Self-Healing","optional":true}],"status":"live","first_seen_at":"2026-11-09T17:35:30Z","employer_posted_date":null,"last_verified_at":"2026-11-09T17:35:30Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Role Summary:\n\nThis role goes beyond traditional QA; the ideal candidate should be capable of validating, evaluating, and improving AI-powered systems, agents, copilots, and LLM-driven workflows while building scalable automation frameworks and intelligent quality solutions. You will work closely with Engineering, AI/ML, Platform, Product, and DevOps teams to ensure reliability, safety, observability, scalability, and quality of modern AI-native applications and agentic systems.\n\nWhat you'll do & how you'll make your mark.:\nAI/LLM Quality EngineeringDesign, validate, and optimize testing strategies for AI-powered applications, copilots, chatbots, RAG pipelines, and autonomous AI agents.\nDevelop AI evaluation (Evals) frameworks for: hallucination detection, groundedness, toxicity and bias validation, response quality, context relevance, prompt regression testing, determinism and consistency, safety and guardrail validation.\nBuild automated validation pipelines for LLM outputs using structured assertions, semantic scoring, embeddings, and evaluation metrics.\nCreate synthetic datasets and benchmark datasets for AI testing scenarios.\nTest multi-agent orchestration workflows and agent memory/context retention.\nValidate MCP (Model Context Protocol) integrations and tool interoperability.\nWork on Context Engineering, Prompt Engineering, and Prompt Optimization strategies.\nEvaluate latency, token usage, model cost optimization, throughput, and scalability.\n\nAutomation EngineeringDevelop and maintain scalable UI/API automation frameworks using: Python (mandatory), Robot Framework (preferred), Playwright/Selenium/Cypress.\nAutomate testing for: REST APIs, GraphQL APIs, event-driven systems, streaming systems, webhooks, microservices, MFE (Micro Frontend) architectures.\nBuild reusable automation libraries, utilities, and AI-assisted testing workflows.\nIntegrate automation into CI/CD pipelines and quality gates.\n\nAI Engineering & Platform ValidationValidate RAG architectures including chunking strategies, embedding quality, retrieval accuracy, re-ranking, vector search relevance.\nWork with vector databases such as: Pinecone, Weaviate, ChromaDB, Qdrant, FAISS.\nBuild or validate AI agents leveraging: LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, OpenAI SDKs.\nValidate AI workflows involving: tool calling, function calling, agent memory, agent planning/execution, multi-modal AI systems, structured outputs.\n\nDevOps, Observability & ReliabilityBuild and maintain quality workflows integrated with: GitHub Actions, Jenkins, GitLab CI/CD, Azure DevOps.\nWork with Docker and Kubernetes environments.\nPerform cloud-based testing and deployments across AWS/Azure/OCI/GCP.\nValidate distributed systems reliability and resiliency.\nMonitor AI system behaviors using observability platforms/logging/tracing tools.\nContribute to shift-left and quality engineering best practices.\n\nWho you are & what you'll need to succeed.:\nBachelor's/Master's degree in Computer Science, Engineering, AI/ML, or related field preferred.\nEquivalent hands-on experience and demonstrated projects are highly valued.\nStrong programming experience in Python.\nStrong understanding of Software Testing & Quality Engineering principles.\nHands-on experience with: AI/LLM testing, Prompt Engineering, Context Engineering, AI Evals, RAG systems, AI Agents.\nExperience with: CI/CD pipelines, Docker & Kubernetes, cloud platforms (AWS/Azure/OCI/GCP), API testing & automation, microservices testing, UI automation.\nExperience with SQL and data validation.\nStrong debugging, analytical, and troubleshooting skills.\nExperience working in Agile/Scrum environments.\n\nGood to have skills:\nExperience with Robot Framework.\nExperience with Playwright.\nKnowledge of: LangChain, LangGraph, LlamaIndex, CrewAI, Semantic Kernel, MCP (Model Context Protocol).\nHands-on experience with: Cursor, GitHub Copilot, Claude, Gemini, OpenAI APIs.\nExperience with vector databases and embeddings.\nExposure to MLOps workflows and ML lifecycle testing.\nKnowledge of performance testing and security testing.\nExperience validating AI governance/compliance requirements.\nExperience with contract testing, chaos testing, or resilience engineering.\nFamiliarity with accessibility testing and cross-browser/device testing.\nExposure to synthetic monitoring and production observability.\n\nBonus Skills (High Impact):\nAI Red Teaming.\nLLM Security Testing (Prompt Injection, Jailbreaks, Data Leakage).\nAgent Reliability & Safety Testing.\nAI Cost Optimization.\nDistributed Systems Testing.\nTest Data Engineering.\nFeature Flag Testing.\nAI Observability & Telemetry.\nReinforcement Learning feedback workflows.\nHuman-in-the-loop evaluation systems.\nKnowledge Graph integrations.\nMulti-modal AI testing (vision/audio/text).\n\nWhat We're Looking For:\nEngineers who actively experiment with AI tools and build side projects, prototypes, PoCs, or open-source contributions.\nStrong problem solvers who can combine traditional QE practices with modern AI-native engineering approaches.\nPassion for innovation, developer productivity, automation acceleration, and intelligent testing.\nAbility to rapidly learn emerging AI frameworks, tools, and architectures.\n\nNice Portfolio Signals:\nCandidates with one or more of the following will stand out:\nGitHub repositories showcasing AI/automation projects.\nAI agents or copilots built personally or professionally.\nContributions to open-source automation or AI ecosystems.\nBlogs, talks, workshops, or technical content on AI/testing.\nExperience integrating AI into SDLC or QE workflows.\nExperience with autonomous testing systems or self-healing automation.\nSkills\nAPI, CrewAI, Cypress, GraphQL, LLM, LangChain, LangGraph, Playwright, Python, RAG, 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