Senior Data Engineer

End Date

Friday 30 October 2026

We Support Flexible Working – Click here for more information on flexible working options

Flexible Working Options

Hybrid Working

Job Description Summary

• Role Title: Senior Data & AI Scientist
• Location: Hyderabad
• Role Type: Hybrid

hy Lloyds Banking Group

Like the modern Britain we serve, we’re evolving. Investing billions in our people, data and tech to transform the way we meet the ever-changing needs of our 26 million customers. We’re growing with purpose. Join us on our journey and you will too.

About this Opportunity

Lloyds Banking Group puts customers at the heart of everything. Data & AI Science forms a key part of our strategy, and as an Senior Data & AI Scientist you will be contributing the largest ever transformation in this history of financial services.

As a Senior Data & AI Scientist, you will lead the development of advanced machine learning models and AI applications. You will work in multidisciplinary teams to explore problem statements, experiment with different approaches, and develop industrialized applications. Your role also involves mentoring junior colleagues and ensuring the delivery of high-quality data science products.

Job Description

Your role would include the following activities:

Design and implement solutions using foundation models (e.g., GPT, PaLM, Claude, Gemini) for tasks such as summarization, Q&A, classification, and generation.

Evaluate model performance, fine-tune pre-trained models, and apply prompt engineering techniques.

Build and deploy autonomous agents capable of reasoning, planning, and interacting with APIs or tools to complete tasks.

Develop and deploy supervised and unsupervised ML models using scalable pipelines.

Integrate ML models into production environments using CI/CD and containerization tools.

Build robust, modular Python applications for data processing, model training, and inference.

Collaborate with Machine Learning Engineers to ensure seamless integration of AI solutions.

Lead code reviews and mentor junior developers on best practices.

Use cloud-native tools (Google Cloud Preferred) for data storage, model training, and orchestration (e.g., Vertex AI, BigQuery, Cloud Functions).

Mentor junior data scientists and engineers in AI techniques and best practices.

Collaborate cross-functionally to align AI initiatives with strategic goals.

What You’ll Need

Master’s or PhD in Computer Science, AI, Data Science, or a related field or equivalent experience.

3 or more years of experience in data science or AI roles, with hands-on experience in foundation models and agent-based systems.

Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers).

Experience with LangChain, AutoGen, or similar agentic frameworks.

Strong understanding of cloud platforms (GCP preferred).

Excellent communication and leadership skills.

Recruitment Priority Skills

We're keen to speak to candidates with experience who have strong skills in :

Machine Learning : Experience to situationally assess and advise on the most appropriate Machine Learning solution to tackle existing and new scenarios. Troubleshoots and fixes model performance issues with the fitted models. Is able to use advanced methods such as forecasting, ensemble methods and model stacking.

Python: Has an holistic understanding of Python and can make intuitive decisions in familiar contexts. Capable of implementing multi-threading and asynchronous programming using tools like threading and asyncio. Optimize performance using profiling tools and apply advanced debugging techniques.

AI Literacy: Can design workflows that integrate AI, anticipates AI behaviour, and adapts usage creatively. Coaches others and contributes to AI strategy discussions.

Foundation Model Application: Independently making decisions about Foundation Model Applications, considering the strengths and limitations of different models. Taking ownership of foundation model-related decisions and tasks, ensuring they align with organisational goals.

Google Cloud Platform: Capable of end-to-end delivery of products on GCP following standard architectures and templates. Takes key decisions on implementation.