Senior Software Engineer - AI

Job summary

Build and deploy production-ready AI experiences including conversational agents, RAG systems, and intelligent automation. Translate ambiguous product problems into scalable, secure, and cost-efficient AI services using a variety of LLMs and backend technologies.

Job details

Senior Applied AI Engineer Who 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. Operate as an AI powered engineer using Cursor, Claude Code, OpenAI Codex, or equivalent coding agents to accelerate design, implementation, testing, debugging, and refactoring. What we’re looking for 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. Nice to have. You should be comfortable going beyond a working demo and understand 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 that compare approaches using measurable outcomes rather than subjective testing alone. 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. How you’ll work You will often start with an ambiguous customer or product goal rather than a detailed implementation plan. You should be able to independently break the problem down, validate assumptions, choose an approach, and drive the solution through production. Turn product goals into testable technical hypotheses and rapidly prototype alternatives when needed. Choose the simplest architecture that solves the problem well rather than using an agent or LLM by default. Define quality, latency, reliability, safety, and cost targets and measure production performance against them. Own implementation across APIs, workflows, data, queues, model integration, evaluations, observability, and production support. Use AI coding agents aggressively to increase engineering velocity while preserving architecture quality, testing discipline, security, and maintainability. What will make you stand out 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. Strong problem solving, excellent engineering judgment, and the ability to independently move from ambiguity to a production quality solution are the foundation for this role. Join us to build the agent-powered backbone of our AI platform—robust, model agnostic, and ready for millions of users. This Job Description includes the essential job functions required to perform the job described above, as well as additional duties and responsibilities. This Job Description is not an exhaustive list of all functions that the employee performing this job may be required to perform. The Company reserves the right to revise the Job Description at any time, and to require the employee to perform functions in addition to those listed above.

What you’ll do

Build and deploy production-ready AI experiences including conversational agents, RAG systems, and intelligent automation. Translate ambiguous product problems into scalable, secure, and cost-efficient AI services using a variety of LLMs and backend technologies.

Requirements

Requires 5+ years of professional software engineering experience with a strong focus on backend and distributed systems. Must have hands-on experience building applied AI applications and proficiency in Python and asynchronous programming.

Listed skills

• Redis · Preferred
• PostgreSQL · Preferred
• prompt engineering · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• FastAPI
• LLM
• RAG
• PostgreSQL
• Redis
• RabbitMQ
• Kafka
• Azure Service Bus
• Distributed Systems
• Asynchronous Programming
• Prompt Engineering
• Vector Databases
• API Development
• AI Agents
• Model Evaluation
• Hallucinations
• Tool Calling
• Claude Code
• Explainable AI (XAI)
• Go-to-Market Strategy
• Langgraph
• Pipelines
• Vector Database
• CI/CD
• Semantic Kernel
• Intelligent Automation
• AI Safety
• Observability
• Workflow Management
• OpenTelemetry
• Technology Ecosystems
• Model Context Protocol (MCP)
• Application Programming Interface (API)
• Artificial Intelligence
• Automation
• Test Automation
• Microsoft Azure
• Back Pressure
• Backbone.js (Javascript Library)
• Management
• Business Systems
• Data Modeling
• Debugging
• Deterministic Methods
• Event-Driven Programming
• Experimentation
• Problem Solving
• Python (Programming Language)
• Maintainability

Job areas

• Software
• Technology
• Engineering
• Data & Analytics
• Software Engineer
• Software Developer / Engineer
• Software Developers