Senior/ Lead AI ML Engineer

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