Senior Software Engineer – AI

🔥 18 hours ago

🌐 Canada, Argentina – Remote

🇨🇦 Canada – Remote 🇦🇷 Argentina – Remote

⏰ Full Time

🟠 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 loosely defined product problems into practical AI solutions • Evaluate different approaches and independently turn the strongest solution into reliable, secure, observable, and cost-efficient production systems • Design and build production AI services using Python, FastAPI, asynchronous workers, PostgreSQL, Redis, queues, model APIs, and external tools • Build reliable tool-calling agents and multi-step workflows interacting with internal APIs, MCP tools, business systems, and knowledge sources • Design event-driven systems with retries, dead-letter handling, idempotency, back pressure, and failure recovery • Build and improve RAG systems covering ingestion, chunking, embeddings, hybrid retrieval, reranking, metadata filtering, context construction, and citations • Experiment with models from OpenAI, Anthropic, Google, xAI, and open-weight ecosystems • Create AI evaluation pipelines using curated datasets, regression tests, retrieval metrics, LLM-as-judge techniques, groundedness checks, and tool-execution evaluation • Diagnose hallucinations, retrieval failures, incorrect tool usage, agent loops, latency issues, provider failures, and unexpected inference costs • Use AI coding agents to accelerate design, implementation, testing, debugging, and refactoring • Turn product goals into testable technical hypotheses and rapidly prototype alternatives • Define quality, latency, reliability, safety, and cost targets and measure production performance • Own implementation across APIs, workflows, data, queues, model integration, evaluations, observability, and production support

🎯 Requirements

• 5 or more years of professional software engineering experience building production backend, distributed, or cloud-based systems • Hands-on experience building Applied AI, LLM, RAG, NLP, or agent-based applications, with meaningful production exposure • Advanced Python skills including FastAPI, asynchronous programming, Pydantic, SQLAlchemy or SQLModel, and production API development • Strong backend and distributed systems fundamentals including REST APIs, concurrency, background processing, caching, reliability, and production debugging • Production experience with RabbitMQ, Kafka, Azure Service Bus, or equivalent queue and messaging architectures • Hands-on experience integrating LLM APIs and building structured output, function calling, tool calling, or agent execution workflows • Experience building at least one RAG or knowledge-grounded system with measurable quality and latency outcomes • Strong PostgreSQL and data modeling skills, with experience using 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 core part of daily software engineering • Comfort understanding how modern AI systems behave, fail, and scale in production • Strong understanding of prompting, structured outputs, tool schemas, context management, model routing, retries, fallbacks, rate limits, and streaming responses • Ability to design experiments and evaluations using measurable outcomes • Understanding of retrieval quality, grounding, hallucination mitigation, prompt injection risks, tool authorization, and practical agent guardrails • Experience with Docker, CI/CD, automated testing, observability, distributed tracing, OpenTelemetry, Langfuse, or similar tooling • Experience with Semantic Kernel, Microsoft Agent Framework, LangGraph, PydanticAI, or equivalent agent frameworks • Experience building or consuming Model Context Protocol tools and servers, or working with model gateways such as LiteLLM • Experience with Azure AI Search, pgvector, Chroma, Kubernetes, Azure, OCI, or similar production AI platform technologies • Experience building high-scale customer-facing AI products, open-weight model inference, AI safety controls, or automated agent evaluation systems