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Role:

We're scaling an AI platform that powers Computer Vision and real-time video analytics, and we need someone to own the architecture, not just contribute to it. Our AI workloads are moving to the cloud, our models need hardening for production, and our engineering practices need a north star. The gap between prototype and production is costing us velocity.

Responsibilities:

• Architect and govern the end-to-end AI platform from data lake to model serving on AWS (SageMaker preferred).
• Lead cloud migration of AI workloads with a cloud-native, containerised approach (Docker, Kubernetes, CI/CD).
• Own the AI roadmap model accuracy, MLOps maturity, observability, and scalability.
• Set engineering standards across ML development, deployment, and monitoring.
• Mentor engineers and data scientists; reduce key-person dependency across the org.
• Drive productionisation, turn research into reliable, monitored, high-performance systems.

Requirements:

• Deep expertise in Computer Vision, Deep Learning, and Video/Real-Time Analytics.
• Fluency in PyTorch and/or TensorFlow, Python, and ML architecture patterns.
• Hands-on with AWS SageMaker, Docker, Kubernetes, CI/CD pipelines.
• Experience with model monitoring, observability tools, and data lake architectures.
• A track record of leading AI strategy, not just executing it.

About the Role

We're looking for a hands-on AI Architect to lead the design and delivery of AI-native applications. You will define the AI stack, establish engineering standards, and drive adoption of AI-assisted development across teams.

Responsibilities

• Architect and build production-grade AI applications.
• Evaluate and standardize AI frameworks, tools, and platforms.
• Design agentic systems, RAG pipelines, and AI workflows.
• Establish AI evaluation, testing, guardrails, and observability practices.
• Drive AI-native development and delivery acceleration initiatives.
• Partner with engineering and product teams on technical roadmap decisions.

Requirements

• 7+ years of software engineering experience.
• Strong experience with LLMs, RAG, embeddings, prompt engineering, and agent frameworks.
• Experience with LangGraph, AutoGen, MCP, or similar ecosystems.
• Hands-on experience with AI evaluations (DeepEval, Ragas), guardrails, and structured outputs.
• Strong Python and TypeScript/Java skills.
• Experience with Kubernetes, CI/CD, IaC, observability, and event-driven architectures.
• Proven experience shipping AI applications to production.