ArniTech Digital Solutions

AI Training / Production-Grade AI Architecture and LLMOps Engineering

Training track

Production-Grade AI Architecture and LLMOps Engineering

Code and architecture patterns for agentic workflows, enterprise RAG, evaluation and guardrails.

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Naming and scope note (checked October 2026). Vendor-neutral. Labs run on a model platform you choose (for example Claude, Amazon Bedrock, Microsoft Foundry or Gemini Enterprise Agent Platform) in a sandbox. It does not lead to a vendor certification.

Best fit: Engineering teams moving AI prototypes into production.

Courses by audience

Engineers, architects and technical teams

Production-Grade AI Architecture and LLMOps Engineering

Duration: 4 days (proposed) · Format: Hands-on engineering cohort: private, or open when scheduled

Who it is for

Senior engineers, architects and technical leads.

Prerequisites

Strong programming skills, API and cloud fundamentals; a sandbox account arranged beforehand.

Learning outcomes

  • Design agentic workflows with bounded tools and deterministic fallbacks
  • Build enterprise RAG with permission-aware retrieval and measured quality
  • Implement an LLMOps pipeline: versioned prompts, evaluation gates, observability and rollback

Curriculum

  • Reference architecture and failure modes
  • Retrieval design, chunking, ranking and access control
  • Agents: tool contracts, state, limits and human approval
  • Evaluation sets, regression gates and red-teaming
  • Guardrails, prompt injection and data leakage
  • Observability, cost and incident response
  • Release process and governance hooks

Hands-on exercise

Build, evaluate and harden a RAG and agent service, then pass it through a release gate.

Takeaway materials

  • Annotated reference code
  • Evaluation harness and dataset templates
  • LLMOps pipeline checklist
  • Architecture decision record templates

Completion outcome

ArniTech completion certificate (not a vendor certification).

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Risk, compliance and security professionals

Assuring LLMOps: Evaluation Evidence and Release Governance

Duration: 1 day (proposed) · Format: Workshop with review exercises

Who it is for

Risk, assurance and security reviewers of AI delivery.

Prerequisites

Familiarity with audit or risk review.

Learning outcomes

  • Judge whether evaluation evidence is adequate
  • Define release criteria and approval authority
  • Audit prompt and model change records

Curriculum

  • What good evaluation looks like
  • Release criteria and sign-off
  • Change control for prompts and models
  • Monitoring and incident evidence

Hands-on exercise

Review a sample evaluation report and decide go, fix or stop.

Takeaway materials

  • Control-mapping worksheet
  • Review checklist for AI use cases
  • Evidence examples to request from delivery teams

Completion outcome

ArniTech completion certificate (not a vendor certification).

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What happens after you enquire

  1. You complete a short form naming the track and audience. It prepares an email draft, and you press Send.
  2. We reply by email with scoping questions: group size, tools, environment and delivery preference.
  3. You receive a written proposal with agenda, duration, format and price, tailored to your teams.
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