Senior Technical Trainer - AI / GenAI / Agentic AI

We are an AI-led, platform-driven Digital Engineering and Enterprise Modernization partner, combining deep technical expertise and industry experience to help our clients anticipate what’s next. Our offerings and proven solutions create a unique competitive advantage for our clients by giving them the power to see beyond and rise above. We work with many industry-leading organizations across the world, including 20 Fortune 50 companies and 4 of the 5 top banks in both the US and India, and numerous innovators across the healthcare ecosystem.

Our disruptor’s mindset, commitment to client success, and agility to thrive in the dynamic environment have enabled us to sustain our growth momentum. Persistent has been recognized across top industry platforms for innovation, leadership, and inclusion. We have delivered 23 sequential quarters of growth with $422.5M in Q3 FY26 revenue, up 4.0% Q-o-Q and 17.3% Y-o-Y growth. Our 26,500+ global team members, located in 18 countries, have been instrumental in helping the market leaders transform their industries. We won the 2025 ISG Star of Excellence™ Award for AI and Data Excellence and were named a Leader in the Everest Group Talent Readiness for Next-generation Data, Analytics and AI Services PEAK Matrix® Assessment 2025.

Our company fosters a values-driven and people-centric work environment that enables our employees to:

• Accelerate growth, both professionally and personally
• Impact the world in powerful, positive ways, using the latest technologies
• Enjoy collaborative innovation, with diversity and work-life wellbeing at the core
• Unlock global opportunities to work and learn with the industry’s best

Let’s unleash your full potential at Persistent - persistent.com/careers

About the Role:

20+ years in IT , with strong architecture experience (solution / enterprise / platform / cloud / integration).

Location: NJ, Bay Area

Role Summary:

Responsible for designing, developing, and delivering advanced technical training programmes. Collaborates with senior stakeholders to identify capability gaps, create learning assets, assess training effectiveness, update curriculum, and mentor junior trainers—while staying current with industry trends and emerging AI technologies.

Persistent Systems is in the phase of accelerating internal and client-facing AI capabilities. For this we are seeking a seasoned technologist and educator to act as a senior AI trainer to lead the trainings. This individual will be responsible for designing and delivering high-impact learning paths across two key tracks:

1. Enterprise Hyper Productivity – anchored around Persistent’s SASVA platform, GenAI for software engineering, automation, and product development
2. Enterprise Business Productivity – focused on strategic applications of AI/GenAI across industries like BFSI, Healthcare, and Technology
3. Enterprise Data Readiness – Data Bricks, Snowflake, Azure, Digital Ocean, AWS.

This is a strategic role requiring a unique blend of deep technical expertise, teaching aptitude, industry orientation, and stakeholder engagement.

Key Responsibilities:

1) AI / GenAI / Agentic AI Enablement (Core)

• Own end-to-end AI/GenAI/Agentic AI training strategy and delivery for architects, tech leads, engineers, QA, and cross-functional personas
• Design hands-on, lab-first programmes covering: agent-first architectures, multi-agent patterns, RAG & vector architecture, evaluation/measurement, token economics, governance/trust/security and responsible AI by design.
• Build practical learning paths for:
• AI tools : Claude Code, Codex, Cursor, GitHub Copilot (plus enterprise adoption practices and developer workflows).
• Agentic AI frameworks & orchestration : LangChain, LangGraph, CrewAI, AutoGen; multi-agent systems, agent orchestration, agents/sub-agents, skills/tools, planning & routing patterns.
• Model adaptation : fine-tuning concepts, when to choose RAG vs fine-tuning , evaluation approaches, monitoring and quality guardrails.
• FDE (Forward Deployment Engineer), MCP (Model Context Protocol) .

2) Architecture-led Training (Depth + Practicality)

• Teach and demonstrate architectural design patterns and anti-patterns for AI systems integrated into real enterprise landscapes (legacy + modern)
• Create reference architectures and reusable blueprints for:
• agent-based solutions, orchestration, tool-use, memory, retrieval, observability, and secure deployment.
• Guide teams on buy vs build decisions for AI platforms/frameworks with clear trade-offs and rationale.

3) Curriculum Engineering & Content Creation

• Develop courseware: decks, instructor guides, lab manuals, coding exercises, assessments, rubrics, capstones, and role-based simulations.
• Curate and refresh content in line with evolving tools and internal capability needs (beginner → advanced progression).

4) Measurement, Quality, and Continuous Improvement

• Define learning outcomes, success criteria, and adoption metrics; assess effectiveness through feedback, assessments, and on-the-job application.
• Build/operate an evaluation approach for labs and agentic solutions (correctness, safety, latency, cost, reliability).

5) Mentoring & Faculty Capability Building

• Mentor and upskill internal trainers/faculty; create train-the-trainer tracks for advanced AI topics and tools.

6) Stakeholder & Business Alignment

• Partner with senior leadership, delivery, pre-sales, and practice leaders to align curriculum with business priorities and project demand.

Technology Coverage (Must-Have Breadth):

The trainer must demonstrate real-world working knowledge across legacy + contemporary + AI-native stacks, including (but not limited to):

• AI/GenAI/Agentic AI : agent-first development, agent orchestration, multi-agent systems, tool/skill patterns, evaluation.
• Agentic frameworks : LangChain, LangGraph, CrewAI, AutoGen; and related ecosystem patterns.
• AI coding assistants : Claude Code, Codex, Cursor, GitHub Copilot (and productivity workflows).
• Python (primary) and ability to teach solution-building with engineering best practices.
• Modern engineering : web applications, microservices, micro-frontends, CI/CD, DevOps/SRE, observability.
• Data & integration : RDBMS/NoSQL, streaming/messaging, event-driven systems, enterprise integration (and modernisation perspectives).
• Cloud : AWS/Azure/GCP for AI workload deployment patterns.

Required Skills & Qualifications:

• 20+ years in IT with proven architecture ownership (solution/enterprise/platform) across multiple technology generations.
• Strong, demonstrable expertise in GenAI and Agentic AI concepts (RAG, tool-use, planning/routing, evaluation, deployment patterns).
• Hands-on ability to build and teach:
• agentic solutions using LangChain/LangGraph/CrewAI/AutoGen;
• code-assistant driven engineering workflows;
• model adaptation basics, including fine-tuning decisioning (conceptual + practical constraints).
• Proven capability to design, develop and deliver training programmes end-to-end, including assessments and effectiveness measurement.
• Excellent facilitation, communication, and stakeholder management skills (senior audiences, architects, delivery leaders).

Preferred / Nice-to-Have

• Client-facing enablement experience (workshops, solutioning, training customer teams).
• Familiarity with AI monitoring/evaluation tooling and best practices for production-grade deployments.
• Experience guiding legacy modernisation using AI and architecting large-scale transformations.

Behavioural Competencies

• Systems thinking, structured problem solving, and pragmatic decision-making
• High learning agility, curiosity, and ability to simplify complex concepts for diverse audiences.
• Mentoring mindset; ability to build a strong faculty bench and community of practice.
• Adaptability – able to adapt to changing situations.