Senior Software Engineer - AI

Senior Applied AI EngineerWho we are.

Newfold Digital is a leading web technology company serving millions of customers globally. Our customers know us through our robust portfolio of brands. We have some of the industry's most prominent and storied go-to-market brands, including Bluehost, HostGator, Domain.com, Network Solutions, Register.com and Web.com. We help customers of all sizes build a digital presence that delivers results. With our extensive product offerings and personalized support, we take pride in collaborating with our customers to serve their online presence needs. The strength of our company lives in the intersection of our people, our customers, and our brands.

What you’ll do & how you’ll make your mark .

We are looking for a hand on Senior Applied AI Engineer who combines strong software engineering with practical AI problem solving. You will 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.

You will take ambiguous customer and product problems, evaluate different approaches, and independently turn the strongest solution into reliable, secure, observable, and cost-efficient production systems. This is an applied engineering role focused on shipping real AI products.

Who you are & what you’ll need to succeed

• Translate loosely defined product problems into practical AI solutions, choosing between LLMs, RAG, tool calling, agents, deterministic workflows, or traditional software.

• 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 that interact with internal APIs, MCP tools, business systems, and knowledge sources.

• Design event driven systems using RabbitMQ, Kafka, Azure Service Bus, or equivalent platforms, including 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, selecting the right model for quality, latency, reliability, and cost.

• 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.