Job title: Senior Technical Trainer AI & Software Engineering
Location: Dallas, TX (onsite)
Duration: 12+months
Experience: 12+ years
Mode of interview: F2F
Role Overview We're seekingan experienced Technical Trainer to deliverengaging, hands-on learning experiences across a large,diverse enterprise. This rolecombines softwareengineering expertise, Agile and DevOpsexperience, and strong facilitation skills. The trainer will develop and deliver technical curricula, coachlearners through practical exercisesand assessments, maintain training materials, and tracklearner progress. Expectedto be well-versed to teach onresponsible useof AI and AI-assisteddevelopment tools across the software-development lifecycle,helping learnersimprove productivity while maintaining security,quality, and humanoversight. KeyResponsibilities Deliverinstructor-led, hands-on training in softwareengineering, Agile,DevOps, cloud, CI/CD, testing, systemdesign, and AI-assisteddevelopment. Translate complexconcepts into clear, practicalinstruction for learnersat varying skill levels. Teachresponsible AI, including privacy,security, bias awareness,output validation,and humanoversight. Teach agentic AIconcepts--including planning, tool use, memory, orchestration,multi-agent workflows, and human approval controls--andwhen to usetheminstead of deterministic automation. Develop andmaintain curricula, labs, codesamples, assessments, diagrams, and digital learning content. Facilitate coding exercises,demonstrations, simulations, andcollaborative problem-solving. Teach object-oriented design,service-oriented architecture, TDD, automated testing, the TestingPyramid,source control, containers, Kubernetes, cloud platforms, and CI/CDtoolchains. Coachlearners on debugging,concurrency, performance, resiliency, reliability, andmaintainability. Adapttraining to distributed systems, high-volume applications,and enterpriseuse cases. Managelearning paths, assessments, learnerprogress, and training records. Partner with engineering leaders and subject-matter expertstokeep content current and deliver consistent learning acrosscohorts. Measure outcomes and continuously improvetraining usinglearnerperformance and feedback. Minimum Qualifications Bachelor's degree in computerscience or a related discipline, or equivalent education andexperience. Sixor moreyears of relevant experience in an Agile environment. Five or moreyears creating anddelivering technical training. Hands-on experience with softwareengineering, TDD,CI/CD,and Agilemethodologies such as ExtremeProgramming, Kanban, or Scrum. Experience using generative AI andAI coding assistantsin software-development or DevOpsworkflows. Practical experience buildingor supporting AI agents and tool-enabledLLM workflows. Understandingof responsible AI, secure data handling, AI-generated coderisks, agent orchestration, tool calling,structured outputs, context management,and human-in-the-loop controls. Experience testingnondeterministic systems and diagnosingagent failures. Preferred Qualifications Experiencedeveloping labs and learning content for AI-assisted softwareengineering. Familiaritywith RAG, embeddings,vector databases, grounding, LLM APIs,model evaluation, andAI governance. Experienceintegrating AI and agentswith applications, APIs, CI/CD pipelines, cloud services,observability platforms, and enterprise knowledgesources. Experience with agent frameworkssuch asSemantic Kernel, AutoGen, LangGraph, or equivalent. FamiliaritywithMCP, agent-to-agent communication,and multi-agentorchestration. Knowledge of agent security,including prompt-injection defenses, permissionboundaries,data-loss prevention, sandboxing,and audit trails. Experience buildingCI/CD pipelinesand applying the Testing Pyramid. Experience withconcurrency, debugging, performance profiling, andoptimization. Strong communication skillsand the ability to simplify systems,solutions, andprocesses. Technical Skills Programming: Java, Python,C#, JavaScript, TypeScript Frameworks: Spring, Spring Boot,FastAPI Frontend: Angular, React Containers and orchestration: Docker, Kubernetes Sourcecontrol and delivery: GitHub, AzureDevOps, GitHub Actions Data: PostgreSQL, MongoDB, Redis Integration andAPIs: Kafka,REST, GraphQL Cloud: Azure, AWS Testingand quality: Selenium, TestNG, Postman,SonarQube, Cypress, JUnit,NUnit, PyTest, Cucumber,Playwright, WireMock, Mockito, Moq Agentic AI: Agent design and orchestration, tool calling, planning, memory and context management, retrieval, multi-agent patterns, MCP, human approvals,evaluation, observability,security guardrails,and cost and latency optimization.
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