Technical Coach

PINNACLE GROUP (Pinnacle Technical Resources); Fort Worth (TX); Hybrid; USD 90,000 - 120,000; Full time

Job summary

Pinnacle Group (Pinnacle Technical Resources) is seeking an experienced Technical Trainer to deliver engaging, hands‑on learning across a large enterprise. You will design curricula, lead labs, and coach learners in Agile, DevOps, cloud, CI/CD, and AI‑assisted development.

The role emphasizes responsible AI, agentic AI concepts, and secure coding practices, with a hybrid setup in Fort Worth, TX. 6+ years in software engineering and 5+ years in technical training are preferred.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related discipline; equivalent education and experience may be considered.
• Six or more years of relevant experience in software engineering or technical delivery within an Agile environment.
• Five or more years of experience creating and delivering technical training.
• Hands‑on experience with software engineering, 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 LLM workflows.
• Strong 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 or AI workflow failures.
• Strong ability to explain complex technical concepts through practical instruction, demonstrations, and hands‑on exercises.

Responsibilities

• Deliver instructor‑led and hands‑on training in software engineering, Agile, DevOps, cloud, CI/CD, testing, system design, and AI‑assisted development.
• Translate complex technical concepts into clear and practical instruction for learners with varying experience levels.
• Teach responsible AI practices, including privacy, security, bias awareness, secure data handling, output validation, and human oversight.
• Teach agentic AI concepts, including planning, tool use, memory, orchestration, multi‑agent workflows, tool calling, and human approval controls.
• Explain when agentic AI is appropriate compared with deterministic automation.
• Develop and maintain curricula, labs, code samples, assessments, diagrams, exercises, and digital learning content.
• Facilitate coding exercises, technical demonstrations, simulations, and collaborative problem‑solving sessions.
• Train learners on object‑oriented design, service‑oriented architecture, TDD, automated testing, the Testing Pyramid, source control, containers, Kubernetes, cloud platforms, and CI/CD toolchains.
• Coach learners on debugging, concurrency, performance, resiliency, reliability, maintainability, and distributed systems.
• Teach software engineering practices for high‑volume applications and enterprise environments.
• Manage learning paths, assessments, learner progress, and training records.
• Partner with engineering leaders and subject‑matter experts to keep training content accurate and current.
• Measure learner outcomes and continuously improve training based on performance data and feedback.
• Diagnose failures in nondeterministic AI systems and agent‑enabled workflows.
• Promote secure and responsible use of AI‑generated code and AI‑assisted development tools.

Job description

Fort Worth , Texas Contract Oct 28, 2026

Position Details:
Job Title:Technical Trainer
Location:Fort Worth, TX (Hybrid: 3 days office)
Duration: Contract with extension

Position Overview

We are seeking an experienced Technical Trainer to deliver engaging, hands‑on learning experiences across a large and diverse enterprise environment. This role combines software engineering expertise, Agile and DevOps experience, AI knowledge, and strong instructional skills.

The Technical Trainer will design and deliver technical curricula, facilitate coding exercises and practical labs, coach learners through assessments, maintain training materials, and track learner progress. The ideal candidate will be experienced in teaching the responsible use of generative AI, AI coding assistants, and agentic AI across the software development lifecycle while emphasizing security, quality, validation, and human oversight.

Key Responsibilities

• Deliver instructor‑led and hands‑on training in software engineering, Agile, DevOps, cloud, CI/CD, testing, system design, and AI‑assisted development.

• Translate complex technical concepts into clear and practical instruction for learners with varying experience levels.

• Teach responsible AI practices, including privacy, security, bias awareness, secure data handling, output validation, and human oversight.

• Teach agentic AI concepts, including planning, tool use, memory, orchestration, multi‑agent workflows, tool calling, and human approval controls.

• Explain when agentic AI is appropriate compared with deterministic automation.

• Develop and maintain curricula, labs, code samples, assessments, diagrams, exercises, and digital learning content.

• Facilitate coding exercises, technical demonstrations, simulations, and collaborative problem‑solving sessions.

• Train learners on object‑oriented design, service‑oriented architecture, TDD, automated testing, the Testing Pyramid, source control, containers, Kubernetes, cloud platforms, and CI/CD toolchains.

• Coach learners on debugging, concurrency, performance, resiliency, reliability, maintainability, and distributed systems.

• Teach software engineering practices for high‑volume applications and enterprise environments.

• Manage learning paths, assessments, learner progress, and training records.

• Partner with engineering leaders and subject‑matter experts to keep training content accurate and current.

• Measure learner outcomes and continuously improve training based on performance data and feedback.

• Diagnose failures in nondeterministic AI systems and agent‑enabled workflows.

• Promote secure and responsible use of AI‑generated code and AI‑assisted development tools.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related discipline; equivalent education and experience may be considered.

• Six or more years of relevant experience in software engineering or technical delivery within an Agile environment.

• Five or more years of experience creating and delivering technical training.

• Hands‑on experience with software engineering, 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 LLM workflows.

• Strong 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 or AI workflow failures.

• Strong ability to explain complex technical concepts through practical instruction, demonstrations, and hands‑on exercises.

Preferred Qualifications

• 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 agents with applications, APIs, CI/CD pipelines, cloud services, observability platforms, and enterprise knowledge sources.

• Experience with agent frameworks such as Semantic Kernel, AutoGen, LangGraph, or equivalent technologies.

• Familiarity with MCP, agent-to-agent communication, and multi‑agent orchestration.

• Knowledge of agent security practices, 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 optimization.