Software Engineer – AI

🕒 September 15

🌐 Canada, Argentina – Remote

🇨🇦 Canada – Remote 🇦🇷 Argentina – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 AI Engineer

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Newfold Digital

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2021

💼 Consulting

📣 Marketing

📦 Logistics

💰 Venture Round on 2021-01

Consulting • Marketing • Logistics

Newfold Digital is a leading web presence solutions provider serving millions of small-to-medium businesses globally. Through its portfolio of brands, including Bluehost, CrazyDomains, HostGator, Network Solutions, Register. com, Web. com, and many others, Newfold Digital helps customers of all sizes build a digital presence that delivers results and adds value to businesses. With extensive product offerings such as domains, website builders, hosting, security, online marketing, professional website design, and SEO services, along with personalized support, Newfold Digital collaborates with its customers to meet their online presence needs.

📋 Description

• Build production AI experiences across Network Solutions, including conversational agents, business and website creation, knowledge and FAQ agents, content experiences, domain discovery, and intelligent automation • Translate product requirements into practical AI solutions using LLMs, RAG, tool calling, agents, deterministic workflows, or traditional software where appropriate • Build production AI services using Python, FastAPI, asynchronous workers, PostgreSQL, Redis, queues, model APIs, and external tools • Implement reliable tool-calling agents and multi-step workflows interacting with internal APIs, MCP tools, business systems, and knowledge sources • Build event-driven workflows with retries, dead-letter handling, idempotency, and failure recovery • Build and improve RAG systems covering ingestion, chunking, embeddings, hybrid retrieval, reranking, metadata filtering, context construction, and citations • Integrate models from OpenAI, Anthropic, Google, xAI, and open-weight ecosystems • Build and run AI evaluations using curated datasets, regression tests, retrieval metrics, LLM-as-judge techniques, groundedness checks, and tool-execution evaluation • Debug hallucinations, retrieval failures, incorrect tool usage, agent loops, latency issues, provider failures, and unexpected inference costs • Break product goals into manageable pieces, validate assumptions, implement solutions, and discuss architecture tradeoffs with senior engineers • Turn product goals into testable technical tasks and prototype alternatives • Measure quality, latency, reliability, safety, and cost and use those signals to improve solutions • Own implementation across APIs, workflows, data, queues, model integration, evaluations, observability, and production support for assigned features • Use AI coding agents to increase engineering velocity while preserving testing discipline, security, maintainability, and code quality

🎯 Requirements

• 3 or more years of professional software engineering experience building production backend, distributed, or cloud-based systems • At least 1 year of hands-on experience building Applied AI, LLM, RAG, NLP, or agent-based applications • Strong Python skills including FastAPI, asynchronous programming, Pydantic, SQLAlchemy or SQLModel, and production API development • Good backend and distributed systems fundamentals including REST APIs, concurrency, background processing, caching, reliability, and production debugging • Hands-on experience with RabbitMQ, Kafka, Azure Service Bus, or equivalent queue and messaging architectures is required • Hands-on experience integrating LLM APIs and building structured output, function calling, tool calling, or agent execution workflows • Experience building or contributing to a RAG or knowledge-grounded system, including retrieval, embeddings, indexing, context construction, and evaluation • Working knowledge of PostgreSQL and data modeling, with exposure to Redis, pgvector, vector databases, or hybrid search technologies • Demonstrated use of AI coding agents such as Cursor, Claude Code, Codex, or equivalent tools as a regular part of software engineering work