Tezo is a new generation Digital & AI solutions provider, with a history of creating remarkable outcomes for our customers. We bring exceptional experiences using cutting-edge analytics, data proficiency, technology, and digital excellence.
Role Summary
We are building an AI Factory to deliver enterprise‑grade, agentic AI solutions that move beyond pilots into sustained business impact. As an AI Engineer, you will be a core builder within a cross‑functional AI Pod, responsible for implementing, testing, and deploying agentic systems into real client workflows. This role is hands‑on and execution‑focused. You will turn architectural designs and product intent into reliable, observable, and production‑ready AI components, working closely with the AI Product Lead, Lead AI Architect, and Automation Engineers.
Key Responsibilities
Agentic System Development
- Build and implement agentic AI components, including:
- Tool‑using agents
- Multi‑step reasoning and execution flows
- Planner, executor, and validator agent patterns
- Translate architectural designs into working, maintainable code
- Ensure agent behavior is bounded, predictable, and aligned with intent
Prompt, Memory & Retrieval Engineering
- Develop prompts as versioned, testable software artifacts
- Implement and tune:
- Retrieval‑augmented generation (RAG)
- Memory and context‑management strategies
- Input and output constraints
- Optimize prompts and retrieval for accuracy, latency, and cost
Evaluation, Testing & Reliability
- Build evaluation harnesses to assess:
- Accuracy and relevance
- Hallucination and failure rates
- Regression across prompt and model changes
- Implement automated testing and validation for AI workflows
- Partner with the Lead AI Architect to monitor drift and degradation over time
Deployment & Operations
- Package and deploy AI components into production environments
- Integrate AI logic into APIs, services, and workflows
- Instrument solutions for:
- Performance
- Usage
- Cost‑to‑serve
- Support production troubleshooting and iterative optimization
Collaboration Within the AI Pod
- Work closely with:
- AI Product Lead to align implementation with acceptance criteria
- Lead AI Architect on design decisions and patterns
- Automation / Integration Engineers to enable real‑world execution
- Contribute to reusable components, templates, and patterns within the AI Factory
Required Qualifications
Experience
- 5–8+ years of experience in software engineering, data engineering, or applied AI development
- Hands‑on experience building and deploying systems into production environments
- Comfort working in agile, fast‑moving delivery teams
AI & Agentic Skills
- Practical experience with:
- Generative AI and LLMs
- Agentic frameworks or orchestration patterns
- Tool calling and action execution
- Strong understanding of common LLM failure modes and mitigation techniques
- Ability to reason about when to rely on AI vs deterministic logic
Technical Skills
- Strong proficiency in Python
- Experience with:
- API development and integration
- Event‑driven or workflow‑based systems
- CI/CD and modern DevOps practices
- Familiarity with cloud‑native architectures (Azure preferred)
Preferred Qualifications
- Experience implementing:
- Retrieval‑augmented generation (RAG)
- Vector databases and embedding strategies
- Exposure to monitoring, observability, or MLOps concepts
- Experience working in enterprise or regulated environments
- Consulting or client‑facing delivery experience
What Success Looks Like
- Agentic components are reliable, testable, and production‑ready
- AI solutions integrate cleanly into real business workflows
- Failures are anticipated, detectable, and recoverable
- Delivery velocity increases through reuse of proven patterns
- Clients experience AI as a dependable system, not a black box
