Senior AI Engineer - Data Agents

Dystematic Limited
London
11 months ago
Applications closed

Related Jobs

View all jobs

Director, Quantitative Imaging or Imaging Biomarkers (London Area)

Director, Quantitative Imaging or Imaging Biomarkers (City of London)

Director, Quantitative Imaging or Imaging Biomarkers

Director, Quantitative Imaging or Imaging Biomarkers

Senior Data Engineer - (Python & SQL)

Senior Data Engineer - (Python & SQL)

We are expanding the AI capabilities of our company and are looking to hire aSenior AI Engineerfocused on buildingData Agents. This role will also involve developing tools to transform plain language questions into actionable insights, including SQL query generation, entity matching, and data visualisations.

If you have a passion for leveraging generative models and are excited about implementing cutting-edge AI solutions, we’d love to have you join our team! You’ll collaborate with experienced developers, data scientists, and product managers to shape the future of AI-powered data applications. We offer a competitive salary and an environment that fosters continuous learning and innovation.

Key Responsibilities

  • DevelopData Agentscapable of interpreting natural language questions into SQL queries, data insights, and visualisations.
  • Create domain-agnostic tools to support the development of Data Agents (e.g., entity matching algorithms).
  • Implement and fine-tune large language models (LLMs) for domain-specific data analysis tasks.
  • Collaborate with cross-functional teams to integrate Data Agents into our Data and AI Operating System.
  • Stay current with the latest AI research and apply novel techniques to solve complex problems.

Requirements

  • MSc or PhD in Data Science, AI, ML, or Computer Science.
  • 5+ years of experience in applied AI, with a focus on natural language processing and data analysis.
  • Experience with generative models, large language models (LLMs), and entity resolution.
  • Experience with LangChain or similar frameworks for building language model applications.
  • Proficiency in Python and SQL, with strong skills in data manipulation and analysis.
  • Expertise in AI frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers.
  • Ability to effectively communicate complex AI concepts, especially to non-technical stakeholders.

Preferred Qualifications

  • Experience with graph databases and knowledge graphs.
  • Familiarity with business intelligence tools and data warehousing concepts.
  • Background in semantic parsing or natural language-to-SQL translation.

Next Steps

Interested in the vacancy? We encourage a diverse workforce and welcome applications from all communities.

#J-18808-Ljbffr

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.