Junior AI Platform Engineer

Job not on LinkedIn

🕒 October 14

🇺🇸 United States – Remote

⏰ Full Time

🟢 Junior

🏗️ Platform Engineer

🚫👨‍🎓 No degree required

👻 Ghost score 12%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.Why this score:

• Open for 27 days.
• Jobs at this company usually fill in 44 days.
• No salary listed.

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LigaData

WebsiteLinkedInAll Job Openings

51 - 200 employees

🤖 Artificial Intelligence

📡 Telecommunications

🤝 B2B

Artificial Intelligence • Telecommunications • B2B

LigaData is a company that provides data and AI-driven telecom products and services tailored for communications service providers. Their offerings include the LigaData Telecom Data Fabric, which unifies and correlates subscriber and operations data, and the LigaData Data Platform, which offers innovative and cost-effective ways to manage and analyze large volumes of data. They also offer AI apps to address telecom data and analytics use cases, and provide various managed services and professional services to support their products. LigaData partners with major cloud providers like Google Cloud Platform, Microsoft Azure, and AWS to enhance their telecom tech solutions with AI and cloud innovations.

📋 Description

• Implement and maintain small Python backend features, API integrations, and utility scripts within established designs • Help connect local and cloud language models to application workflows, including structured responses and tool calls • Prepare and validate training or evaluation datasets, checking formats, missing values, duplicates, and documented data provenance • Write and maintain unit and integration tests; reproduce bugs and verify fixes under senior review • Run established model evaluations and bounded performance tests, recording inputs, settings, results, failures, and resource measurements • Investigate application logs and request traces, document findings, and escalate unclear or cross-component issues with reproducible examples • Assist with Docker-based development, CI checks, and deployment verification using approved runbooks and supervision • Follow authorization, data-handling, and secret-management practices; keep sensitive information out of logs and shared artifacts • Update setup guides, test instructions, and change notes • Participate in code reviews and team discussions • Contribute through scoped implementation, evidence collection, testing, and documented experiments • Deliver scoped features and bug fixes that pass relevant checks and senior code review • Produce reproducible test and evaluation results, recording failures and limitations • Follow data-access and operational procedures and raise risks or blockers promptly • Maintain useful documentation and gradually handle routine debugging and implementation with less supervision

🎯 Requirements

• Working knowledge of Python, including functions, classes, exceptions, modules, and common data structures • Understanding of HTTP APIs, JSON, and client-server request flows, with experience building or consuming an API in a project • Basic SQL skills and familiarity with relational databases and structured data • Ability to use Git for branches, commits, and pull requests, and to work from the command line • Familiarity with automated testing and a systematic approach to reproducing and debugging problems • Basic understanding of LLM prompts, context, structured outputs, and validation of generated answers and code • Ability to explain code, respond to review feedback, and communicate progress and blockers clearly • Practical evidence of programming ability through personal, academic, open-source, internship, or professional projects • Previous production AI experience is not required • Preferred: exposure to FastAPI or another Python web framework, PostgreSQL, and Docker • Preferred: project involving an LLM API, embeddings, retrieval, or a locally hosted model • Preferred: familiarity with pytest, GitHub Actions, Linux, or basic asynchronous programming • Preferred: experience cleaning or transforming data with pandas, SQL, CSV, JSON, or Parquet • Preferred: exposure to React or TypeScript, Arabic-English AI workflows, or introductory machine-learning coursework

🏖️ Benefits

• Mentoring and code review from senior engineers • Learning opportunities in reliable tool calling, model evaluation, observability, and concurrent application workflows • Potential exposure to supervised fine-tuning experiments, GPU inference, and larger data-processing systems • Progressive responsibility as skills develop

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