Artificial Intelligence Engineer
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Technical Depth
- Advanced proficiency in Python (including typing, packaging, asynchronous programming, and performance optimisation) and SQL, with experience writing and optimising complex production queries, window functions, and large-scale data joins.
- Hands-on experience with at least two key Generative AI components, including:Retrieval-Augmented Generation (RAG) solutions, covering chunking strategies, hybrid search, and result re-ranking.
- Agentic AI workflows using frameworks such as LangGraph, CrewAI, AutoGen, or custom-built orchestration frameworks.
- Model fine-tuning and PEFT techniques, including LoRA and QLoRA, for both open-source and proprietary models.
- Deployment and management of vector databases in production environments, such as Pinecone, Weaviate, Qdrant, pgvector, or FAISS.
- Experience with LLM orchestration frameworks including LangChain, LlamaIndex, Semantic Kernel, or similar technologies.
- Strong foundation in traditional Machine Learning, including feature engineering, model selection, experimentation, and model evaluation.
- Proven cloud engineering experience across AWS, Azure, or GCP, including the deployment, scaling, governance, and cost optimisation of AI/ML workloads using services such as SageMaker, Azure ML, or Vertex AI.
- Solid software engineering practices, including clean coding standards, modular architecture, automated testing (pytest/unittest), version control, and collaborative Git workflows.
- Experience with MLOps and DevOps tooling, including Docker (essential), alongside at least one of the following:CI/CD platforms (GitHub Actions, GitLab CI, Azure DevOps)
- Workflow orchestration tools (Airflow, Prefect, Dagster)
- Container orchestration platforms (Kubernetes)
- Familiarity with AI monitoring and observability tooling such as Langfuse, Arize, MLflow, Weights & Biases, or custom evaluation and logging frameworks to ensure reliability and performance in production environments.
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