As Technical Lead – Data Science & Generative AI, you drive the design, development and deployment of machine learning, deep learning and generative AI solutions across platforms and client programmes. You work alongside data scientists, data engineers and business stakeholders to ship scalable, production-ready AI/ML applications, shape the technical roadmap for AI initiatives, and mentor the team that builds it.

1. Models in production • Build, train, tune and deploy ML and deep learning models using TensorFlow or PyTorch and scikit-learn. • Own model evaluation, optimisation, monitoring and governance — robustness, fairness, drift and scale, not just offline accuracy. • Do the unglamorous work well: preprocessing, wrangling and feature engineering with pandas and NumPy. 2. GenAI and LLM applications • Develop GenAI applications — conversational agents, summarisation, RAG pipelines, document intelligence. • Design retrieval and prompting strategies that hold up on messy enterprise data — and recognise when fine-tuning is the better answer. • Instrument outputs so quality is measurable rather than anecdotal. 3. Pipelines and engineering rigour • Partner with data engineering to design scalable data and model pipelines for training and inference. • Enforce reproducibility, testing and code quality across the team’s work. 4. Leadership and communication • Mentor data scientists, run technical reviews and promote coding excellence. • Present findings and AI strategy to non-technical stakeholders in language that drives a decision.

• 8–10 years in data science and applied AI, including experience leading a small technical team. • Expert Python: advanced OOP, data structures, API design and testing. • Strong ML/DL delivery with TensorFlow and scikit-learn, and fluency in pandas and NumPy. • Practical LLM and GenAI experience: Hugging Face Transformers, LangChain or LlamaIndex. • Solid statistical foundations: regression, time-series, hypothesis testing and anomaly detection. • Communication that bridges technical and business teams — clear, specific and actionable. • A proven mentoring record and a bias for shipping over perfecting. GOOD TO HAVE • PyTorch for advanced deep learning and LLM fine-tuning. • MLOps in practice: MLflow, Docker, Kubernetes and CI/CD. • Vector databases — FAISS, Pinecone, Weaviate or Milvus. • Cloud ecosystems (AWS, Azure, GCP) and big data frameworks (Spark, Hadoop). • Explainability and fairness tooling such as SHAP and LIME.