Associate Data Scientist - Agentic AI & Machine Learning

About the role

Addo AI is looking for an Associate Data Scientist with 2-3 years of hands-on experience in both traditional machine learning and Agentic AI. The role involves designing, building and deploying ML models and LLM-powered agentic systems that solve real business problems for clients. It requires strong Python skills, a solid grounding in statistics and ML, and practical experience building AI agents and workflows.

Key responsibilities

• Design, develop and deploy AI agents and multi-step agentic workflows using LLMs, tool calling and orchestration frameworks.
• Build Retrieval-Augmented Generation (RAG) pipelines, including document processing, embeddings, vector databases and retrieval optimisation.
• Develop, train and evaluate traditional ML models for classification, regression, forecasting, clustering and recommendation.
• Collect, clean and explore data, and perform feature engineering on structured and unstructured data.
• Integrate agents with external tools, APIs, databases and enterprise systems.
• Apply prompt engineering, guardrails and evaluation techniques to improve the accuracy, reliability and safety of LLM-based solutions.
• Choose between classical ML, LLM-based agents or a hybrid approach for each problem.
• Write production-quality Python and SQL code, and support deployment, monitoring and optimisation of models and agents.
• Work with engineering, product and client teams to turn business requirements into technical solutions, and stay current with Agentic AI, LLMs and ML.

Required profile

• Bachelor's or Master's degree in Computer Science, Data Science, AI, Statistics or a related field.
• 2-3 years of professional experience in data science or machine learning, including hands-on work on Agentic AI or LLM-based projects.
• Good analytical, problem-solving and communication skills, able to explain technical concepts to non-technical stakeholders.

Required skills

• Strong Python, with Pandas, NumPy and Scikit-learn; XGBoost/LightGBM and PyTorch/TensorFlow are a plus.
• LLM and agent frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI or AutoGen.
• Experience with LLM APIs (OpenAI, Anthropic, Azure OpenAI, Gemini) and open-source models.
• Vector databases (Pinecone, FAISS, Chroma, Weaviate, pgvector) and RAG systems.
• SQL and experience with FastAPI/Flask, Docker and cloud platforms (AWS, Azure, GCP).
• Nice to have: LLM evaluation tools (LangSmith, Langfuse, RAGAS), fine-tuning and Hugging Face, MLOps (MLflow, CI/CD), and enterprise AI delivery in banking, finance or retail.