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# Data Scientist - GenAI - Consultant

- **Company:** Infosys Limited
- **Location:** London, UK
- **Salary:** £75,137.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Amazon Web Services, Computer Vision, Microsoft Azure, Big Data, Cloud Computing, Continuous Integration, Distributed Systems, Python (Programming Language), NoSQL, NumPy, Power BI, Tensorflow, Standard Sql, Search Technologies, Tableau (Software), Enterprise Search, Pinecone, Feature Engineering, Pytorch, LangChain, Retrieval-Augmented Generation, Large Language Models, Grafana, Model Validation, Llamaindex, Pandas, Scikit Learn, Kubernetes, Weaviate, Machine Learning Operations, FAISS, Api Design, Google Gemini, Semantic Kernel, Docker
- **Published:** October 15, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5927580213

## About the Role

Essential Skills:

 * Strong Python/R (pandas/NumPy; ML libs such as scikit-learn; DL frameworks TensorFlow/PyTorch).
 * Experience with LLM/RAG toolchains (e.g., LangChain, LlamaIndex, Semantic Kernel) and vector search (e.g., Pinecone, Weaviate, FAISS, Azure AI Search).
 * Hands-on experience with AWS Bedrock and Sagemaker would be highly desirable
 * Experience with GenAI platforms (e.g., OpenAI API, Anthropic, Gemini, Llama or equivalents).
 * Exposure to big data/distributed computing and pipeline/feature engineering.
 * LLM safety & governance (hallucination mitigation, grounded responses, audit trails)
 * Degree in a quantitative field
 * Hands-on exposure in building models and setting up ML Ops using Promithius/Grafana etc.

Preferred Skills:

 * Cloud ML experience (AWS/GCP/Azure).
 * Strong SQL; experience with visualisation tools (Tableau/Power BI or Python viz).
 * Specialisms: NLP / computer vision / time series.
 * NoSQL familiarity.
 * Quant / trading analytics engineering practices
 * Time-series forecasting (prices, demand, blend outcomes, scheduling effects)
 * Optimisation / simulation (planning, blending, logistics constraints)
 * Model risk controls (bias/leakage checks, backtesting discipline, monitoring/drift)
 * CI/CD, deployment, monitoring; Docker/Kubernetes.
 * Experiment design and randomised trials.
 * MSc with PhD a plus

Personal attributes

 * Analytical, pragmatic problem-solver; outcome-oriented.
 * Self-directed, able to prioritise and juggle multiple workstreams.
 * Clear communicator who can simplify complexity.
 * Collaborative, curious, continuous learner.

Given that this is just a short snapshot of the role we encourage you to apply even if you don't meet all the requirements listed above. We are looking for individuals who strive to make an impact and are eager to learn. If this sounds like you and you feel you have the skills and experience required, then please apply now.

## Description

We are looking for highly skilled Data Scientists to join our team. As a Data Scientist, you'll design and deliver GenAI solutions (LLM/RAG) and applied ML components, taking prototypes through to production with strong evaluation, observability and governance. You will work closely with cross-functional teams, including data engineers, analysts, and business stakeholders, to turn data into actionable strategies that drive business outcomes.

The ideal candidate should have a strong background in applied ML and GenAI delivery (LLM/RAG), including evaluation, deployment, and governance in production environments., * Design and deliver GenAI solutions including LLM/RAG (retrieval strategy, embeddings, vector stores, prompt flows, grounding) for enterprise use cases.

 * Evaluate and improve solution quality using offline/online metrics (quality, latency, cost) and iterate based on feedback.
 * Harden solutions for production with observability/monitoring, tracing, guardrails, safety controls, and reliability practices
 * Build and integrate model endpoints into products and workflows (APIs/services), partnering with engineering through to deployment.
 * Work across cloud platforms (Azure/AWS/GCP) integrating storage, compute, orchestration, and model/runtime components.
 * Assess data readiness for modelling/RAG (fitness, quality, access) and define remediation requirements
 * Collaborate in cross-functional squads (DS/DE/engineering/product) and contribute to reusable assets and ways of working.
 * Communicate clearly with stakeholders on trade-offs, evaluation results, risks, and adoption actions.
 * Own end-to-end workstream delivery, lead stakeholder conversations, mentor others. (more senior levels)
 * Shape solution direction and quality bar, coach teams, contribute to sales pursuits/bids and accelerators (most senior levels)