Data Analyst – AI/ML & Generative AI

About the role

We are looking for a motivated Data Analyst who will help design, develop and deploy AI/ML solutions and cloud‑based data platforms. The role sits at the intersection of data engineering, machine learning and generative AI, turning data into actionable insights that drive business outcomes.

Key responsibilities

• Develop, evaluate and deploy machine‑learning models for classification, regression, recommendation and NLP use cases.
• Integrate generative AI capabilities such as large language models and retrieval‑augmented generation into internal applications and workflows.
• Design AI agents and conversational interfaces (chatbots, virtual assistants) using modern agent‑based frameworks.
• Apply prompt‑engineering techniques to optimise LLM performance for specific business tasks.
• Build, monitor and maintain data pipelines and ETL/ELT workflows.
• Query, clean and preprocess structured and unstructured data for model training and reporting.
• Create dashboards and visualisations that clearly communicate insights to technical and non‑technical audiences.

Required profile

• 1–3 years of hands‑on experience in data analytics, machine learning, software engineering or cloud development (including internships or academic projects).
• Bachelor’s degree in Computer Science, Data Science, Engineering, Statistics or a related quantitative field.
• Experience working in Agile/Scrum teams and exposure to MLOps practices such as model versioning and experiment tracking.
• Ability to tackle ambiguous problems iteratively, test hypotheses and refine solutions based on feedback.

Required skills

• Python for data processing, model development and automation.
• SQL for data querying and transformation.
• Familiarity with JavaScript, C# or PowerShell (optional).
• ML frameworks: Scikit‑learn, TensorFlow, PyTorch.
• Generative AI tools and LLM APIs: OpenAI, Claude/Anthropic, LLaMA, Hugging Face.
• Prompt‑engineering and retrieval‑augmented generation techniques.
• Agent‑based frameworks: LangChain, LlamaIndex, AutoGen.
• MLOps tools such as MLflow for experiment tracking.