Senior AI/Data Engineering Consultant

Telefonica Tech
London
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Consultant - AI & Data, Financial Services, Data Platforms, Data Engineer, BCM, Edinburgh

Senior Consultant - AI & Data, Financial Services, Data Platforms, Data Engineer, BCM, Edinburgh

Senior Consultant, Data Engineer, AI&Data, UKI, London

Consultant - Senior Consultant, Palantir Foundry Data Engineer, AI & Data, Defence & Security

Consultant - Senior Consultant, Palantir Foundry Data Engineer, AI & Data, Defence & Security

Senior Data Engineer & AI Transformation Consultant

Job Description

We are looking for people that will guide us in our growth, innovate and mentor. We need you to help us break and create the rules to continue to be a place admired for our people, culture and innovation - and to help us in being a place that everyone wants to work, and no one wants to leave! 

The role will vary depending on the project but will primarily focus on the delivery of enterprise-level solutions in the Artificial Intelligence, Data Science / Machine Learning and Data Engineering arena.  

This is a client-facing position, so the ideal candidate must be comfortable speaking with clients and some occasional travel. 

Our offices are in Farnham and London. The role can be based at either location. Induction, training and company meets are done at both offices. When we can, we generally get together at either of the offices either a Wednesday or a Friday. 

RESPONSIBILITIES 

  • Working on projects that utilise the Microsoft Azure technology stack across domains such as AI, Data Engineering, Data Science & Machine Learning.  
  • Satisfying the expectations and requirements of customers, both internal and external 
  • Supporting others in their development 
  • Contributing to the internal and external community 


Qualifications

  • Industry experience in delivering Microsoft Azure solutions, with a good grounding in all associated areas 
  • Proven written and spoken English 
  • Strategic and operational decision-making skills 
  • Outstanding interpersonal skills 
  • Ability and attitude towards investigating and sharing new technologies 
  • Ability to guide, direct or influence people 
  • Ability to identify opportunities, issues and risks 
  • Willingness to learn based on feedback 
  • Able to help others develop
  • Ideally degree educated - computer science, data analysis, AI & Machine Learning etc 
  • Microsoft certified (nice to have) 

TECHNICAL SKILLS 

Peoples skills vary and that’s great because the role varies. You should be comfortable with at least 3 of the core technologies below and have an interest in at least 4 others within the core/supporting/principles. Current Microsoft / Databricks certifications are useful but not mandatory – we’ll help you get those! 

Core: 

  • Data Manipulation (SQL, Pandas, Pyspark) 
  • Azure AI (Azure AI Foundry, AI Search, Document Intelligence, AI Services) 
  • Data Science & Machine Learning (Databricks, Python, SKLearn, XGBoost, MLFlow, EDA) 
  • Familiarity with LLMs (OpenAI, Prompt Engineering, LangChain) 
  • Relevant Azure Data & Computation services (ADLS, ADF, Databricks, SQL Databases) 

Supporting: 

  • Azure ML Services 
  • React / CSS / JavaScript 
  • Azure infrastructure 
  • R, Powershell 
  • Kubernetes / Docker 
  • Tensorflow / Pytorch 

Principles: 

  • Data Modelling 
  • Data Science 
  • Data Warehouse Theory 
  • Data Architecture 
  • Master Data Management 



Additional Information

We don’t believe hiring is a tick box exercise, so if you feel that you don’t match the job description 100%, but would still be a great fit for role, please get in touch.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.