Software Dev Engineer 2, IES Prime

Amazon
London
11 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Senior Manager, Head of Data Engineering

Senior Manager, Head of Data Engineering

Hybrid Data Engineering Lead & Delivery Architect

Software Engineer - AI MLOps Oxford, England, United Kingdom

Software Engineer III- Data Engineer, Java/Python

IES Prime is building a team to take Prime experience of customers to the next level by building capabilities that are relevant for Prime as well as non-Prime customers in IN and other EMs. Our Development team plays a pivotal role in this program, with the mission to build a comprehensive solution for the India Prime business. This is a rare opportunity to be part of a team that will be responsible for building a successful, sustainable and strategic business for Amazon Prime and to expand the coverage of recurring payments for Prime in India and take it to new emerging markets.


The ideal candidate will be instrumental in shaping the product direction and will be actively involved in defining key product features that impact the business. You will work with Sr. and Principal Engineers at Amazon Prime to evolve the design and architecture of the products owned by this team. You will be responsible to set up and hold a high software quality bar besides providing technical direction to a highly technical team of Software Engineers.


As part of this team you will work to ensure Amazon.in Prime experience is seamless and has the best shopping experience. It’s a great opportunity to develop and enhance experiences for Mobile devices first. You will work on analyzing the latency across the various Amazon.in pages using RedShift, DynamoDB, S3, Java, and Spark. You will get the opportunity to code on almost all key pages on the retail website, building features and improving business metrics. You will also contribute to reducing latency for customers by reducing the bytes on wire and adapting the UX based on network bandwidth. You will be part of a team that obsesses about the performance of our customer’s experience and enjoy flexibility to pursue what makes sense. Come enjoy an exploratory and research-oriented team of Cowboys working in a fast-paced environment, who are always eager to take on big challenges.

BASIC QUALIFICATIONS

- 3+ years of non-internship professional software development experience
- 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- 3+ years of Video Games Industry (supporting title Development, Release, or Live Ops) experience
- Experience programming with at least one software programming language

PREFERRED QUALIFICATIONS

- 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
- Bachelor's degree in computer science or equivalent


Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visitthis linkfor more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

#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.

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

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

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

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.