Software Developer for GCS

Tekever
Cardigan
1 week ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Senior Software Developer

Software Developer in Test

Lead Software Developer (Polygot - ASP.Net, C#, Java, Spring)

Senior Python Developer

ML/AI Software Engineer


Are you ready to revolutionise the world with TEKEVER?

Join TEKEVER, the European leader in unmanned technology, where groundbreaking advancements meet unparalleled innovation. Our industry-leadingSurveillance-as-a-Servicesolution delivers real-time intelligence, enhancing maritime safety and saving lives. By setting new benchmarks in intelligence services, data, and AI technologies, TEKEVER is reshaping global safety and driving the future.

At TEKEVER, we enable critical decision-making with mission-oriented solutions and transformative innovation. If you're passionate about technology and eager to make a tangible difference, TEKEVER is the place for you!

Were seeking a talentedSoftware Engineerwith expertise inGIS developmentfocused on maps, navigation, and geospatial intelligence (GEOINT). This role involves designing and maintaining GIS applications for web or desktop platforms, ensuring exceptional performance and seamless user experiences.

What will be your responsabilities:

  • Application Development:Design and develop GIS applications for web or desktop platforms, with a focus on maps, navigation, and GEOINT.
  • Feature Collaboration:Work with cross-functional teams to define and implement innovative features.
  • Performance Optimization:Ensure applications are highly responsive and optimized for performance.
  • Debugging:Identify and resolve software issues effectively.
  • Service Integration:Develop and integrate GIS services and APIs.
  • Code Quality:Conduct code reviews to maintain high standards of code quality.

Profile and Requirements:

  • Education:Bachelors degree in Computer Science, Software Engineering, or a related field.
  • Language Requirements: Advanced proficiency in English, with proven fluency at the C2 level.
  • GIS Expertise:Proven experience in GIS development, particularly with maps, navigation, and GEOINT.
  • Technical Skills:Proficiency in programming language C#.
  • GIS Tools:Hands-on experience with tools like ArcGIS, QGIS, or MapInfo.
  • Technologies:Familiarity with desktop frameworks (WPF).
  • Spatial Data:Knowledge of spatial databases and geospatial data formats.
  • Problem-Solving:Strong problem-solving skills and attention to detail.
  • Teamwork:Excellent communication and collaboration abilities.
  • Cloud Solutions:Experience with cloud-based GIS solutions.
  • Machine Learning:Knowledge of machine learning applications for geospatial data.
  • DevOps:Familiarity with DevOps practices and CI/CD pipeline
What we have to offer you:
  • Attractive Salary Package & Benefits:Competitive salary and pension scheme.
  • Generous Leave Policy:25 days annual leave plus UK Bank Holidays.
  • Employee Assistance Programme:Comprehensive support for your well-being.

Do you want to know more about us ?

Visit our LinkedIn page athttps://www.linkedin.com/company/tekever/

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Machine‑Learning Jobs for Non‑Technical Professionals: Where Do You Fit In?

The Model Needs More Than Math When ChatGPT went viral and London start‑ups raised seed rounds around “foundation models,” many professionals asked, “Do I need to learn PyTorch to work in machine learning?” The answer is no. According to the Turing Institute’s UK ML Industry Survey 2024, 39 % of advertised ML roles focus on strategy, compliance, product or operations rather than writing code. As models move from proof‑of‑concept to production, demand surges for specialists who translate algorithms into business value, manage risk and drive adoption. This guide reveals the fastest‑growing non‑coding ML roles, the transferable skills you may already have, real transition stories and a 90‑day action plan—no gradient descent necessary.

Quantexa Machine‑Learning Jobs in 2025: Your Complete UK Guide to Joining the Decision‑Intelligence Revolution

Money‑laundering rings, sanctioned entities, synthetic identities—complex risks hide in plain sight inside data. Quantexa, a London‑born scale‑up now valued at US $2.2 bn (Series F, August 2024), solves that problem with contextual decision‑intelligence (DI): graph analytics, entity resolution and machine learning stitched into a single platform. Banks, insurers, telecoms and governments from HSBC to HMRC use Quantexa to spot fraud, combat financial crime and optimise customer engagement. With the launch of Quantexa AI Studio in February 2025—bringing generative AI co‑pilots and large‑scale Graph Neural Networks (GNNs) to the platform—the company is hiring at record pace. The Quantexa careers portal lists 450+ open roles worldwide, over 220 in the UK across data science, software engineering, ML Ops and client delivery. Whether you are a graduate data scientist fluent in Python, a Scala veteran who loves Spark or a solutions architect who can turn messy data into knowledge graphs, this guide explains how to land a Quantexa machine‑learning job in 2025.

Machine Learning vs. Deep Learning vs. MLOps Jobs: Which Path Should You Choose?

Machine Learning (ML) continues to transform how businesses operate, from personalised product recommendations to automated fraud detection. As ML adoption accelerates in nearly every industry—finance, healthcare, retail, automotive, and beyond—the demand for professionals with specialised ML skills is surging. Yet as you browse Machine Learning jobs on www.machinelearningjobs.co.uk, you may encounter multiple sub-disciplines, such as Deep Learning and MLOps. Each of these fields offers unique challenges, requires a distinct skill set, and can lead to a rewarding career path. So how do Machine Learning, Deep Learning, and MLOps differ? And which area best aligns with your talents and aspirations? This comprehensive guide will define each field, highlight overlaps and differences, discuss salary ranges and typical responsibilities, and explore real-world examples. By the end, you’ll have a clearer vision of which career track suits you—whether you prefer building foundational ML models, pushing the boundaries of neural network performance, or orchestrating robust ML pipelines at scale.