Senior Software Engineer

AIDA Intelligent Solutions
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Software Engineer, Machine Learning

Senior Data Engineer (AI & MLOps, AWS, Python)

Senior Machine Learning Engineer

Senior MLOps Engineer

Senior MLOPs Engineer

Lead Software Engineer - Agentic AI/Machine Learning

JOB TITLE:Senior Software Engineer C# .NET


SALARY: £60,000-65,000


LOCATION: Hybrid – blend remote work with our vibrant office in Aldwych, central London.


About us

We are a dynamic startup at the forefront of tech innovation, crafting groundbreaking products that empower retailers to thrive in the digital era. We're searching for exceptional, entrepreneurial-minded developers ready to propel their careers forward in an exciting and fast-paced environment.


Why you'll love working here

·Innovative projects: Be part of cutting-edge developments that reshape the retail landscape.

·Collaborative culture: Join a close-knit team where your voice matters and creativity is celebrated.

·Career growth: Opportunities to advance and refine your skills with continuous learning and mentoring.


Your role and responsibilities

· Innovate and create: Develop new features and functionalities for our software applications tailored to customer or internal specifications.

· Solve complex problems: Write software that offers logical, mathematical, and data-centric solutions to intricate business challenges.

·Debug and improve: Identify, troubleshoot, and resolve defects in application code to ensure optimal performance.

·Collaborate and integrate: Work closely with business and technical colleagues to design, develop, and document seamless data integration methods.

·Deliver excellence: Meet delivery deadlines for assigned projects, prioritising value and checking assumptions to maximise business impact.

· Mentor and learn: Share your expertise by coaching peers while continuously learning from them.


What we're looking for

· Tech savvy: Proficient in C#, HTML, CSS, and SQL with strong debugging capabilities.

·Design and document: Hands-on experience in application and database design and documentation.

· Team player: Thrive in a collaborative environment, working independently when needed and integrating back into the group.

· Communicator: Excellent verbal and written communication skills, adept at discussing technical issues with diverse audiences.



Essential qualifications

·Educational background: B.Sc. (2:1 minimum) in Computer Science or a related field from a well-respected institution.

·Technical expertise: Strong knowledge of C# and SQL, with a solid understanding of data treatment and ETL processes.


Ideal candidates will also have

·Cutting-edge skills: Familiarity with Blazor and Microsoft Azure technologies, including Azure Functions, Azure Data Lake, and Azure Data Factory/Synapse Analytics.

·DevOps proficiency: Experience with CI/CD, particularly Azure Pipelines, and Bicep/Arm.

·Passion for AI: Knowledge of or eagerness to learn Text Mining, Text Analytics, Natural Language Processing, and Python.


Ready to make an impact?

If you're ready to join a forward-thinking team and contribute to innovative solutions that transform the retail industry, we want to hear from you.

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.