Senior AI Engineer

Newfold Digital; Canada; On-site; CAD 120,000 - 180,000; Full time

Job summary

Newfold Digital is seeking a hands-on Senior Applied AI Engineer to translate product needs into production AI systems across Network Solutions. You will build reliable AI services, collaborate with senior engineers, architects, and product partners, and continuously improve AI products with measurable results.

This role focuses on practical AI problem solving—LLMs, RAG, tool calling, and agent workflows with Python, FastAPI, PostgreSQL, and Redis.

Qualifications

• 5+ 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, with production experience strongly preferred.
• 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.

Responsibilities

• 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 that interact with internal APIs, MCP tools, business systems, and knowledge sources.
• Build event driven workflows using RabbitMQ, Kafka, Azure Service Bus, or equivalent platforms, including 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, and help select models based on quality, latency, reliability, and cost.
• 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.
• Operate as an AI powered engineer using Cursor, Claude Code, OpenAI Codex, or equivalent coding agents to accelerate implementation, testing, debugging, refactoring, and documentation.

Skills

Python

FastAPI

Asynchronous programming

REST APIs

Distributed systems

RAG

Tool calling

AI coding agents

PostgreSQL

Redis

Tools

RabbitMQ

Kafka

Azure Service Bus

Redis

pgvector

Docker

CI/CD

OpenTelemetry

Langfuse

Cursor

Claude Code

Codex

Job description

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 we're looking for

We are looking for a hands on Senior Applied AI Engineer who combines strong software engineering fundamentals with practical AI problem solving. You will help 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 work with senior engineers, architects, and product partners to turn customer and product problems into reliable, secure, observable, and cost efficient AI systems. This is an applied engineering role focused on building, measuring, and continuously improving real AI products.

What you'll do & how you'll make your mark

• 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 that interact with internal APIs, MCP tools, business systems, and knowledge sources.
• Build event driven workflows using RabbitMQ, Kafka, Azure Service Bus, or equivalent platforms, including 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, and help select models based on quality, latency, reliability, and cost.
• 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.
• Operate as an AI powered engineer using Cursor, Claude Code, OpenAI Codex, or equivalent coding agents to accelerate implementation, testing, debugging, refactoring, and documentation.

Who you are & what you'll need to succeed

5 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, with production experience strongly preferred. 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.

Nice to have.

You should be comfortable going beyond a working demo and understand the core engineering patterns needed to make AI systems reliable in production. Understanding of prompting, structured outputs, tool schemas, context management, retries, fallbacks, rate limits, caching, and streaming responses. Ability to use experiments and evaluations to 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, logging, observability, distributed tracing, OpenTelemetry, Langfuse, or similar tooling.

How you'll work

You will often receive a product goal or technical direction rather than a fully specified implementation. You should be able t