Senior Technical Coach

We’re seeking an experienced Technical Trainer to deliver engaging, hands-on learning experiences across a large, diverse enterprise. This role combines software engineering expertise, Agile and DevOps experience, and strong facilitation skills. The trainer will develop and deliver technical curricula, coach learners through practical exercises and assessments, maintain training materials, and track learner progress. Expected to be well-versed to teach on responsible use of AI and AI-assisted development tools across the software-development lifecycle, helping learners improve productivity while maintaining security, quality, and human oversight.

KeyResponsibilities

• Deliver instructor-led, hands-on training in software engineering, Agile, DevOps, cloud, CI/CD, testing, system design, and AI-assisted development.
• Translate complex concepts into clear, practical instruction for learners at varying skill levels.
• Teach responsible AI, including privacy, security, bias awareness, output validation, and human oversight.
• Teach agentic AI concepts–including planning, tool use, memory, orchestration, multi-agent workflows, and human approval controls–and when to use the min stead of deterministic automation.
• Develop and maintain curricula, labs, code samples, assessments, diagrams, and digital learning content.
• Facilitate coding exercises, demonstrations, simulations, and collaborative problem-solving.
• Teach object-oriented design, service-oriented architecture, TDD, automated testing, the Testing Pyramid, source control, containers, Kubernetes, cloud platforms, and CI/CD tool chains.
• Coach learners on debugging, concurrency, performance, resiliency, reliability, and maintain ability.
• Adapt training to distributed systems, high-volume applications, and enterprise use cases.
• Manage learning paths, assessments, learner progress, and training records.
• Partner with engineering leaders and subject-matter experts to keep content current and deliver consistent learning acrosscohorts.
• Measure outcomes and continuously improve training using learner performance and feedback.

• Bachelor’s degree in computer science or a related discipline, or equivalent education and experience.
• Six or more years of relevant experience in an Agile environment.
• Five or more years creating and delivering technical training.
• Hands-on experience with software engineering, TDD, CI/CD, and Agile methodologies such as Extreme Programming, Kanban, or Scrum.
• Experience using generative AI and AI coding assistants in software-development or DevOps workflows.
• Practical experience building or supporting AI agents and tool-enabled LLM workflows.
• Understanding of responsible AI, secure data handling, AI-generated code risks, agent orchestration, tool calling, structured outputs, context management, and human-in-the-loop controls.
• Experience testing nondeterministic systems and diagnosing agent failures., * Experience developing labs and learning content for AI-assisted software engineering.
• Familiarity with RAG, embeddings, vector databases, grounding, LLM APIs, model evaluation, and AI governance.
• Experience integrating AI and agents with applications, APIs, CI/CD pipelines, cloud services, observability platforms, and enterprise knowledge sources.
• Experience with agent frameworks such as Semantic Kernel, AutoGen, Lang Graph, or equivalent.
• Familiarity with MCP, agent-to-agent communication, and multi-agent orchestration.
• Knowledge of agent security, including prompt-injection defences, permission boundaries, data-loss prevention, sandboxing, and audit trails.
• Experience building CI/CD pipelines and applying the Testing Pyramid.
• Experience with concurrency, debugging, performance profiling, and optimization.
• Strong communication skills and the ability to simplify systems, solutions, and processes.

Technical Skills

• Programming: Java, Python, C#, JavaScript, TypeScript
• Frameworks: Spring, Spring Boot, Fast API
• Frontend: Angular, React
• Containers and orchestration: Docker, Kubernetes
• Source control and delivery: GitHub, Azure DevOps, GitHub Actions
• Data: PostgreSQL, MongoDB, Redis
• Integration and APIs: Kafka, REST, Graph QL
• Cloud: Azure, AWS
• Testingand quality: Selenium, TestNG, Postman,SonarQube, Cypress, JUnit,NUnit, PyTest, Cucumber,Playwright, WireMock, Mockito, Mo
• 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.