Artificial Intelligence Engineer

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.