Software engineer ii (aws infrastructure) Work, Jobs in Scotland (with Salaries)

AI Engineer II
Zonda Home —Glasgow
- £55,000 a year
- Full-time
- Computer Science
- Engineering
- Master's degree
Quick apply
Software Engineer II (AWS Infrastructure)
JPMorganChase —Glasgow
- Full-time
- Scripting
- Financial services
- AI
1d
Cloud Software Engineer II
JPMorganChase —Glasgow
- Full-time
- AWS
- Software development
- Agile
2d

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AI Engineer II
Zonda Home —Glasgow
Built and deployed at least two LLM-based or NLP-heavy product in a real setting likely using agentic frameworks like LangGraph, LangChain, AutoGen etc.
£55,000 a year
Quick apply
Software Engineer II (AWS Infrastructure)
JPMorganChase —Glasgow
You will be part of a cross-functional team of engineers and product owners building the Spectrum Portfolio Management Fixed Income product, working primarily…
1d

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software engineer ii (aws infrastructure) jobs near scotland

AI Engineer II

Zonda Home -
Glasgow

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Job details

Full-time
£55,000 a year

Qualifications

Cloud infrastructure
Law
Computer Science
Stakeholder collaboration
Relational databases
Requirement elicitation
Stakeholder liaison
Engineering
Kubernetes orchestration
NoSQL
Git
Master's degree
AWS
Maths
Docker
Bachelor's degree
CI/CD pipelines
Product development
Data management
Machine learning modelling
Continuous integration
Natural language processing
Software development
Technical writing
APIs
Design documentation
Business requirements
Flask
AI
DevOps implementation
Communication skills
Analytics

Full job description

Job Title: AI Engineer II

Department: Data Science

Reports to: SVP, Data Science

£55,000

Job Overview

The AI Engineer II is a mid-level position responsible for engineering AI based products and maintenance. As a mid-level engineer in this role, you will work closely with senior engineers and non-technical business stake holders, contributing to the entire lifecycle of building and maintaining emerging LLM-based products. An important aspect of this role would be product design and execution bearing cost efficiency and speed. The ideal candidate would have skills in software programming, building agentic systems, mathematics, and DevOps (including container operations, CI/CD, and cloud engineering). Good communication skills with an ability to demystify LLMs and agentic systems is a must, as stakeholders in AI products include software engineering teams as well as non-technical business partners.

Responsibility

Design, develop, and optimise agentic AI systems.
Work with Product/business stakeholders to capture and establish requirements for AI products.
Steer consolidation of dataset requirements, acquiring data, management, and version control for AI applications.
Monitor AI products in production, setting metrics to identify performance (accuracy / retrieval rates / hit rates), and establish corrective measures for restoring performance.
Identify and implement appropriate tools for monitoring AI product performance in production.
Ownership of technical documentation related to design, model selection, experiments, and production infrastructure.
Continual learning and self-improvement with a focus on latest trends, techniques, and best practices in AI.

Qualifications & Skills

Need to have.

Bachelor's degree in computer science, Engineering, or a related field.
3+ years of experience in AI product development or Machine Learning
Proficient in Python.
Built and deployed at least two LLM-based or NLP-heavy product in a real setting likely using agentic frameworks like LangGraph, LangChain, AutoGen etc.
Strong mathematical, analytical, and problem-solving skills.
Experience with retrieval systems, embeddings, and vector DBs like Weaviate or Pinecone.
Ability to structure and execute an Agentic AI project from start to completion.
Excellent communication and teamwork skills; ability to work in a team.
Experience with cloud computing platforms like AWS.
Familiarity with containerization and orchestration tools like Docker and Kubernetes.
Experience with version control systems like Git.
Ability to leverage coding agents for accelerated software development

Nice to have.

Masters in a specific field such as Statistics, Data Science, Machine Learning, or AI.
Knowledge of SQL and NoSQL databases including construction of queries, query optimisation, and schema design.
API development using standard tools such as FastAPI or Flask.
Good understanding of Machine Learning algorithms and models (Language processing models such as GPT, BERT, etc).

Quick apply

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AI Engineer II