AI Prototyper & AIOps Engineer – Applied AI Systems

If you’re driven by the challenge of rapidly prototyping AI ideas, architecting scalable
pipelines, and bringing cutting-edge models into real workflows, this is your place.
We’re searching for an AI Prototyper & AIOps Engineer with a rare combination of
creativity, hands-on technical depth, and systems thinking. In this role, you’ll turn concepts
into running prototypes, design the infrastructure that powers them, and help shape the
next generation of applied AI solutions for our clients.

Description
As an AI Prototyper & AIOps Engineer, you will rapidly test ideas, build proof-of-concepts,
and operationalize AI models across diverse environments. You will work across LLMs, RAG
pipelines, multimodal models, forecasting systems, and cloud-native architectures. You will
help define standards for model deployment, security, monitoring, and continuous
improvement ensuring reliability and scalability.

Key Responsibilities
• Build rapid prototypes using LLMs, RAG, embeddings, and multimodal models.
• Design and implement end-to-end AIOps pipelines for training and deployment.
• Stand up cloud infrastructure in GCP/Azure for scalable AI workloads.
• Integrate structured, unstructured, and telemetry-style data into models.
• Implement monitoring, observability, and automated evaluation systems.
• Collaborate with PMs, architects, and engineers to define feasibility.
• Produce technical documentation and contribute to delivery frameworks.
• Experiment with new AI techniques and translate innovation into action.

Minimum Qualifications
• Bachelor’s degree in CS, Engineering, or related.
• 5+ years in ML Engineering, AIOps, or similar applied AI functions.
• Strong Python, Docker, Kubernetes, CI/CD, and cloud experience.
• Familiarity with LLMs, open-source models, vector DBs, and RAG.
• Strong ability to prototype quickly and work with ambiguity.
• Experience integrating data sources at scale.

Preferred Qualifications
• Experience with MLOps/AIOps observability systems.
• Prior consulting or customer-facing delivery experience.
• Experience with MLX or Apple Silicon optimization workflows.
• Passion for applied AI, experimentation, and rapid iteration.

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