Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

AI Engineer / Data Scientist

Daxtra Technologies Ltd
Musselburgh
2 weeks ago
Applications closed

Related Jobs

View all jobs

Ai Engineer / Data Scientist

AI Engineer / Data Scientist

AI Engineer (data science & software)

Engineer: Data Science

Engineer: Data Science

Senior Data Scientist (UK)

About Us:
Daxtra is at the forefront of AI-driven innovation, leveraging cutting-edge technologies to develop intelligent solutions that enhance business outcomes. We are looking for an AI Engineer to work closely with our AI/ML team, contributing to the development of scalable AI models and applications under the guidance of the Senior AI Manager.

Role Overview:
As an AI Engineer, you will be responsible for developing, deploying, and optimizing AI models while collaborating with data scientists and software engineers. You will work with technologies such as deep learning, embeddings, vector search, and Generative AI to build robust AI-powered solutions.

Key Responsibilities:

  • Develop and optimize AI/ML models with a focus on Generative AI, embeddings, and vector search.
  • Implement deep learning models using frameworks like TensorFlow, PyTorch, or JAX.
  • Collaborate with data scientists to integrate AI models into production systems.
  • Maintain and enhance AI pipelines, ensuring efficiency and scalability.
  • Conduct research on the latest AI advancements and implement innovative solutions.
  • Assist in fine-tuning large language models (LLMs) and retrieval-augmented generation (RAG) systems.
  • Optimize model performance and work on deployment strategies using cloud-based AI solutions.
  • Support MLOps best practices to streamline AI development workflows.
  • Stay updated with the latest advancements in machine learning by regularly reviewing academic literature and research papers. Capable of translating theoretical insights into practical, real-world solutions.

Required Qualifications:

  • Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.
  • 3+ years of experience in AI/ML development.
  • Generative AI experience with Hugging Face, fine-tuning open-source LLMs like Mistral/Llama/Gemma/Phi/Qwen, vLLM, Text Generation Inference, unsloth, LoRA, adapters, DPO, ORPO, hugging face inference endpoints, LlamaIndex
  • Experience with embeddings, vector databases, and deep learning models.
  • Hands-on experience with cloud AI services (AWS, GCP, Azure) and developing microservices using FastAPI, Flask, or Django, with expertise in containerized deployment (Docker)
  • Knowledge of software engineering principles and best practices for AI integration.
  • Strong problem-solving skills and ability to work in a team environment.

Preferred Qualifications:

  • Experience working with large-scale AI applications and personalization engines.
  • Familiarity with production-scale vector databases (e.g., QDrant, Pinecone, Weaviate).
  • Understanding of AI model interpretability and ethical AI considerations.
  • Exposure to real-time AI applications and MLOps workflows.

Why Join Us?

  • Work alongside industry experts on cutting-edge AI projects.
  • Opportunity to grow and advance in a fast-paced, innovative environment.
  • Competitive compensation, benefits, and professional development opportunities.
  • Flexible work environment with remote-friendly options.

Join us in shaping the future of AI! Apply today at https://www.daxtra.com/join-daxtra/
#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.

Jobs

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Jobs

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Jobs

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.