AI/ML Platform Engineer

21h ago

⚠️ Before applying, please carefully review all the job requirements

Job Overview

This listing is for an AI/ML Platform Engineer position. The work arrangement is listed as remote. For salary, the listing states rs,. The listing places its emphasis on REST APIs, Git and GitHub, Python, Docker, AWS, Artificial Intelligence and LLMs. The listing is aimed at the following profile: 5+ years. For general context: REST APIs let the front end and back end of an application exchange data in a structured way. Git tracks code changes so teams can work on the same project without overwriting each other. Interested candidates can review the full details below and apply using the link provided on this page.

★ REST APIs: Designing and consuming HTTP endpoints so a front end, mobile app or another service can exchange data.
★ Git and GitHub: Tracking changes, working on branches, opening pull requests and resolving conflicts so a team can share code safely.
★ Python: Writing readable scripts and backend or data code using Python's standard library and common packages.
★ Docker: Packaging an application and its dependencies into containers so it runs the same way everywhere.
★ AWS: Using cloud platform services for hosting, storage and databases instead of managing physical servers.
★ Artificial Intelligence: A general understanding of how AI systems learn from data, where they are useful, and where they can be unreliable.
★ LLMs: Understanding how large language models generate text from prompts, and why their output still needs to be checked.
★ Generative AI: The concepts behind systems that generate text, images or code, and how such features can be added to a product.

★ REST APIs: HTTP methods and status codes, authentication basics such as tokens, validation and error responses
★ Git and GitHub: branching and merging, pull requests and code review, resolving merge conflicts
★ Python: core data structures, functions and modules, virtual environments and pip
★ Docker: Dockerfile basics, images versus containers, docker compose
★ AWS: core compute and storage services, permissions and access control, basic deployment
★ Artificial Intelligence: supervised versus unsupervised learning, common real-world use cases, limitations and bias
★ LLMs: prompting basics, context limits and hallucinations, using an LLM API
★ Generative AI: common model types, prompt design, responsible use

This role may be suitable for candidates who match the skills and experience described in the requirements. Applicants with working knowledge of REST APIs, Git and GitHub, Python, Docker, AWS, Artificial Intelligence and LLMs may feel well prepared for what the listing describes. A remote arrangement may suit people who work well independently and communicate clearly in writing. This is editorial guidance only, and the employer makes the final decision on suitability.

Applicants may want to highlight relevant experience with REST APIs, Git and GitHub, Python and Docker in their CV. Because version control is mentioned, a GitHub profile with a few well-described repositories can make your skills easier to verify. If you have used AI tools or built small projects with language models, briefly describe what you built and how you checked the results. Keep your CV to the point and tailor the summary to this role rather than sending a generic version. These are general tips, not requirements stated by the employer.

These suggested questions can help you prepare for an interview by focusing on the skills, responsibilities, and technical areas relevant to this role. They are for preparation only and are not confirmed employer questions.
Q: What makes an API RESTful, and how do you choose HTTP methods and status codes for different operations?
Q: How do you resolve a merge conflict in a shared repository?
Q: How do lists, tuples, sets, and dictionaries differ in Python, and when would you use each?
Q: What problem does Docker solve, and how do an image and a container differ?
Q: Which cloud services have you used for hosting or storage, and how did you choose them?
Q: Where could AI add value to a typical web application, and what risks should you plan for?
Q: How would you handle authentication and input validation for a REST API you are building?
Q: What is the difference between git merge and git rebase, and when would you use each?
Q: How do you stay organised and communicate clearly when working remotely?

Ideal Candidates Fill the Application Form: Apply Here