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Senior AI Engineer

Senior AI Engineer

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Overview

We’re hiring a Senior AI Engineer to design, build and operate production-grade AI systems end to end.

This role is focused on applied AI engineering - not research in isolation. You will take ideas from concept through architecture, implementation, deployment and monitoring in real-world environments.

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Requirements

  • 4+ years building distributed systems in production environments
  • Strong experience developing and deploying ML-powered software using frameworks such as PyTorch or TensorFlow
  • Deep understanding of the ML development lifecycle - data ingestion, experimentation, validation, deployment and observability
  • Hands-on experience building, fine-tuning and optimising Large Language Models and transformer-based architectures
  • Practical experience with tooling such as Hugging Face and commercial model APIs
  • Experience building data-intensive AI applications using modern deep learning approaches
  • Familiarity with agentic AI frameworks and orchestration tools (e.g. LangChain, AutoGen, LangGraph, LlamaIndex, Semantic Kernel, CrewAI or similar)
  • Experience designing multi-agent workflows for goal-oriented AI systems
  • Experience building batch and real-time ML pipelines in AWS environments (e.g. SageMaker, ECS, EKS, Airflow or equivalent tooling)
  • Familiarity with LLM observability and evaluation tooling (e.g. Langsmith, Ragas, Weights & Biases or similar)
  • Strong communication skills and ability to collaborate across engineering teams

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    Responsibilities

  • Design and ship scalable AI systems in production
  • Develop and optimise advanced NLP and LLM-based solutions
  • Frame ambiguous problems into well-defined ML architectures
  • Build robust data pipelines for structured and unstructured data
  • Take models from proof-of-concept to hardened, reliable production services
  • Implement monitoring, evaluation and performance tracking for deployed models
  • Optimise inference performance and system scalability
  • Contribute to architectural decisions across ML infrastructure and platform design

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