AI/ML Engineer

Role Overview

As an AI/ML Engineer at Rivero, you will play a central role in architecting and delivering scalable, production-grade AI solutions. Your work will span from infrastructure to deployment, with a strong emphasis on operational excellence, fast iteration, and applying large language models to real-world problems.

Key Responsibilities/Tasks

In the AI Engineering Domain:

• Design, implement, and deploy production-ready, scalable AI/ML systems, including multimodal foundation model solutions.
• Build robust APIs, evaluation pipelines, infrastructure, and observability layers.
• Rapidly prototype and assess new AI frameworks, libraries, and tools to improve system performance, developer velocity, or product capabilities, then transition to well-architected, maintainable systems.
• Collaborate with product, data, and engineering teams to translate business problems into AI-powered solutions.
• Contribute to building and evolving the internal ML platform and toolset, including CI/CD for ML, and experiment tracking and evaluation.
• Embrace spec-driven development, where we own the specs and design intent and LLMs handle much of the implementation, always with a human in the loop.

What We Are Looking For

We're looking for an AI Engineer who's passionate about building robust systems, experimenting quickly, and shipping value through AI. Above all, you're an AI optimist: you reach for these tools first, push their limits, and get energized by what they make possible rather than waiting to be convinced.

Skills & Competencies

You'll thrive in this role if you have:

• Strong Python programming skills and experience with ML/AI libraries such as PyTorch, Hugging Face Transformers, LangChain and LangGraph.
• Experience deploying and maintaining production-ready ML systems, including monitoring, testing, and continuous delivery.
• Expertise with LLMs, vector databases, retrieval-augmented generation (RAG), and building agentic systems with tool use and orchestration.
• A structured approach to evaluating LLM performance and understanding prompt behaviour across model variants and versions.
• Hands-on experience with cloud platforms (GCP, AWS, or Azure). Infrastructure-as-code or container orchestration tools are an advantage.
• Ability to iterate quickly with prototypes, then transition to well-architected, maintainable systems.
• Collaborative mindset and excellent communication skills for working across functions and disciplines.

Additional Requirements:

• This is an on-site role