Technical Trainer – Software Engineering, DevOps & Agentic AI

Pinnacle Group, Inc.; Fort Worth (TX); Hybrid; USD 90,000 - 120,000; Full time

Job summary

Pinnacle Group, Inc. is seeking a technically hands-on Technical Trainer to design and deliver enterprise-grade training across software engineering, DevOps, and AI domains.

You will create curricula, lead practical labs, assess learners, and track progress while emphasizing responsible AI use, security, and code quality. The role blends instructional expertise with deep engineering knowledge in SDLC tooling and modern practices.

Qualifications

• Bachelor's degree in Computer Science or related field.
• 6+ years of experience in an Agile environment.
• 5+ years of experience developing and delivering technical training.
• Hands-on software engineering experience including TDD and CI/CD.
• Experience using Generative AI and AI coding assistants in software development.
• Knowledge of agent orchestration and human-in-the-loop controls.

Responsibilities

• Design and deliver instructor-led training across software engineering, DevOps, and AI.
• Develop curricula, labs, code samples, and assessments.
• Coach learners of varying levels and track progress.
• Collaborate with engineering leaders to keep content current across cohorts.
• Measure training effectiveness and continuously improve content.

Skills

Technical Training

Agile / Scrum

DevOps

Generative AI

Agentic AI

Curriculum Design

Public Speaking

Coaching

Education

Bachelor's degree in Computer Science or related field

Tools

CI/CD tooling

Kubernetes

Git

Cloud platforms

Job description

Technical Trainer – Software Engineering, DevOps & Agentic AI

Ft. Worth TX 76155- onsite hybrid near DFW airport

Technical Trainer – Software Engineering, DevOps & Agentic AI

Role Overview

We are seeking an experienced Technical Trainer to develop and deliver engaging, hands-on technical training across a large, diverse enterprise environment. This role combines software engineering expertise, Agile and DevOps experience, and strong instructional and facilitation skills.

The ideal candidate will design and deliver technical curricula, lead practical exercises and assessments, coach learners at varying skill levels, maintain training materials, and track learner progress.

A key focus of this position is teaching the responsible use of Generative AI, AI-assisted development tools, and Agentic AI throughout the software development lifecycle (SDLC). The trainer will help engineering teams improve productivity while maintaining security, code quality, reliability, and appropriate human oversight.

Key Responsibilities

Technical Training & Curriculum Development

• Deliver instructor-led, hands-on training in software engineering, Agile, DevOps, cloud technologies, CI/CD, automated testing, system design, and AI-assisted development.
• Translate complex technical concepts into clear, practical instruction for learners with varying levels of experience.
• Develop and maintain technical curricula, hands-on labs, code samples, assessments, diagrams, and digital learning materials.
• Facilitate coding exercises, technical demonstrations, simulations, and collaborative problem-solving sessions.
• Manage learning paths, assessments, learner progress, and training records.
• Partner with engineering leaders and subject-matter experts to ensure training content remains current and consistent across learning cohorts.
• Measure training effectiveness through learner performance and feedback, continuously improving instructional content and delivery.

Software Engineering, Agile & DevOps

• Teach object-oriented design, service-oriented architecture, test-driven development (TDD), automated testing, and the Testing Pyramid.
• Provide instruction on Agile methodologies, including Scrum, Kanban, and Extreme Programming (XP).
• Deliver practical training on source control, containerization, Kubernetes, cloud platforms, and CI/CD toolchains.
• Coach learners on debugging, concurrency, performance optimization, resiliency, reliability, and maintainability.
• Adapt technical training to distributed systems, high-volume applications, and enterprise use cases.

Generative AI & AI-Assisted Development

• Teach developers how to use Generative AI and AI coding assistants effectively throughout the software development lifecycle.
• Demonstrate AI-assisted coding, debugging, testing, documentation, and DevOps workflows.
• Promote responsible AI practices, including data privacy, security, bias awareness, output validation, and human oversight.
• Educate learners on AI-generated code risks, secure data handling, and appropriate review and approval processes.
• Teach Agentic AI concepts, including planning, tool calling, memory, context management, orchestration, and multi-agent workflows.
• Explain when agent-based solutions are appropriate versus traditional deterministic automation.
• Develop practical exercises demonstrating AI agents, tool-enabled LLM workflows, and human-in-the-loop approval controls.
• Provide instruction on testing nondeterministic AI systems, diagnosing agent failures, and improving reliability.
• Introduce agent security, evaluation, observability, and operational best practices.

Minimum Qualifications

• Bachelor's degree in Computer Science or a related technical discipline, or equivalent education and professional experience.
• 6+ years of relevant experience working in an Agile environment.
• 5+ years of experience developing and delivering technical training.
• Hands-on software engineering experience, including TDD, CI/CD, and Agile methodologies such as Scrum, Kanban, or Extreme Programming.
• Experience using Generative AI and AI coding assistants in software development or DevOps workflows.
• Practical experience building or supporting AI agents and tool-enabled large language model (LLM) workflows.
• Understanding of responsible AI principles, secure data handling, and risks associated with AI-generated code.
• Knowledge of agent orchestration, tool calling, structured outputs, context management, and human-in-the-loop controls.
• Experience testing nondeterministic systems and diagnosing AI agent failures.
• Strong communication, presentation, facilitation, and technical coaching skills.

Preferred Qualifications

• Experience designing hands-on labs and training materials focused on AI-assisted software engineering.
• Familiarity with Retrieval-Augmented Generation (RAG), embeddings, vector databases, grounding techniques, LLM APIs, model evaluation, and AI governance.
• Experience integrating AI agents with enterprise applications, APIs, CI/CD pipelines, cloud services, observability platforms, and enterprise knowledge sources.
• Experience with agent orchestration frameworks such as Semantic Kernel, AutoGen, LangGraph, or equivalent technologies.
• Familiarity with Model Context Protocol (MCP), agent-to-agent communication, and multi-agent orchestration.
• Knowledge of AI agent security, including prompt-injection defenses, 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 application optimization.
• Strong communication skills and the ability to simplify complex systems, technical solutions, and engineering processes.

Technical Skills

Programming Languages

• Spring, Spring Boot, FastAPI

Frontend Technologies

• Angular, React

Containers & Orchestration

Source Control, DevOps & CI/CD

Databases & Data Technologies

Integration & APIs

• Kafka, REST APIs, GraphQL

Testing & Quality Engineering

• Selenium, TestNG, Postman, SonarQube, Cypress
• JUnit, NUnit, PyTest, Cucumber
• Playwright, WireMock, Mockito, Moq

Generative AI & Agentic AI

• AI-assisted software development and coding assistants
• LLM APIs and agent frameworks
• Agent design, planning, and orchestration
• Tool calling and structured outputs
• Memory and context management
• Retrieval-Augmented Generation (RAG)
• Multi-agent workflows and orchestration patterns
• Human-in-the-loop approvals
• Agent evaluation, testing, and observability
• Responsible AI, security guardrails, and governance
• Cost, latency, and performance optimization

Ideal Candidate Profile

The ideal candidate is a technically hands-on software engineering professional with proven experience developing and delivering enterprise technical training. They should be comfortable teaching modern development practices, Agile and DevOps methodologies, and emerging AI technologies to engineers with different skill levels.

Successful candidates will combine strong instructional capabilities with practical experience in Generative AI, AI-assisted development, Agentic AI frameworks, and enterprise software engineering, enabling them to deliver relevant, engaging, and immediately applicable technical learning experiences.