Senior AI Engineer (Applied AI)

Location: Remote in Ontario, Nova Scotia, or Argentina

Company

hirify.global is a web technology company serving millions of customers through brands including Bluehost, HostGator, Domain.com, Network Solutions, Register.com, and Web.com.

What you will do

• Translate product requirements into production AI solutions using LLMs, RAG, tool calling, agents, deterministic workflows, and traditional software where appropriate.
• Build AI services with Python, FastAPI, asynchronous workers, PostgreSQL, Redis, queues, model APIs, and external tools.
• Implement tool-calling agents and multi-step workflows integrating internal APIs, MCP tools, business systems, and knowledge sources.
• Build event-driven workflows with RabbitMQ, Kafka, Azure Service Bus, or equivalent platforms, including retries, dead-letter handling, idempotency, and failure recovery.
• Develop and improve RAG systems, model integrations, and AI evaluations covering retrieval, embeddings, reranking, citations, regression testing, groundedness, and tool execution.
• Debug hallucinations, retrieval failures, incorrect tool usage, agent loops, latency issues, provider failures, and unexpected inference costs while operating AI coding agents such as Cursor, Claude Code, and Codex.

Requirements

• 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; production experience is strongly preferred.
• Strong Python skills, including FastAPI, asynchronous programming, Pydantic, SQLAlchemy or SQLModel, and production API development.
• 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.
• 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.
• Regular professional use of AI coding agents such as Cursor, Claude Code, Codex, or equivalent tools.

Nice to have

• Understanding of engineering patterns for reliable production AI systems, including prompting, structured outputs, tool schemas, context management, retries, fallbacks, rate limits, caching, and streaming responses.
• Ability to compare approaches using experiments, evaluations, and 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, logging, observability, distributed tracing, OpenTelemetry, Langfuse, or similar tooling.
• Experience with Semantic Kernel, Microsoft Agent Framework, LangGraph, PydanticAI, MCP tools and servers, model gateways such as LiteLLM, Azure AI Search, pgvector, Chroma, Kubernetes, Azure, OCI, or similar production AI technologies.
• Experience with customer-facing AI products, open-weight model inference, AI safety controls, automated evaluations, or high-scale backend platforms.

Culture & Benefits

• Work remotely from Ontario, Nova Scotia, or Argentina.
• Work with senior engineers, architects, and product partners on customer and product problems.
• Own implementation across APIs, workflows, data, queues, model integration, evaluations, observability, and production support.
• Prioritize simple, maintainable solutions and use deterministic approaches when they are more appropriate than agents or LLMs.
• Measure quality, latency, reliability, safety, and cost to continuously improve production systems.