Senior AI Engineer

This is a fully remote position, open to applicants in Canada, +1 more country.

📋 Description

• Develop and deploy AI-driven solutions across Network Solutions, including conversational agents, website creation, business applications, knowledge and FAQ agents, content experiences, domain discovery, and intelligent automation.

• Convert product requirements into actionable AI solutions utilizing LLMs, RAG, tool calling, agents, deterministic workflows, or traditional software as suitable.

• Create AI services in production environments using Python, FastAPI, asynchronous workers, PostgreSQL, Redis, queues, model APIs, and external tools.

• Implement dependable tool-calling agents and multi-step workflows that interact with internal APIs, MCP tools, business systems, and knowledge sources.

• Develop event-driven workflows utilizing RabbitMQ, Kafka, Azure Service Bus, or similar platforms, incorporating retries, dead-letter handling, idempotency, and failure recovery mechanisms.

• Enhance RAG systems, focusing on ingestion, chunking, embeddings, hybrid retrieval, reranking, metadata filtering, context construction, and citations.

• Integrate models from OpenAI, Anthropic, Google, xAI, and open-weight ecosystems, selecting models based on criteria like quality, latency, reliability, and cost.

• Conduct AI evaluations using curated datasets, regression tests, retrieval metrics, LLM-as-judge techniques, groundedness checks, and tool-execution assessment.

• Troubleshoot issues related to hallucinations, retrieval failures, incorrect tool usage, agent loops, latency problems, provider failures, and unexpected inference costs.

• Utilize coding agents such as Cursor, Claude Code, OpenAI Codex, or similar tools for implementation, testing, debugging, refactoring, and documentation purposes.

• Transform product objectives into testable technical tasks and prototype alternatives.

• Assess quality, latency, reliability, safety, and cost, using these metrics to refine solutions.

• Take ownership of implementation across APIs, workflows, data, queues, model integration, evaluations, observability, and production support for designated features.

• Collaborate with senior engineers, architects, and product partners to design reliable, secure, observable, and cost-effective AI systems.

⛳️ Requirements

• Minimum of 5 years of professional software engineering experience in building production backend, distributed, or cloud-based systems.

• At least 1 year of hands-on experience developing Applied AI, LLM, RAG, NLP, or agent-based applications; production experience is highly preferred.

• Proficient in Python, including expertise in FastAPI, asynchronous programming, Pydantic, SQLAlchemy or SQLModel, and production API development.

• Solid understanding of backend and distributed systems, including REST APIs, concurrency, background processing, caching, reliability, and production debugging.

• Required hands-on experience with RabbitMQ, Kafka, Azure Service Bus, or equivalent queue and messaging systems.

• Proven experience integrating LLM APIs and developing structured output, function calling, tool calling, or agent execution workflows.

• Experience contributing to or building RAG or knowledge-grounded systems, focusing on retrieval, embeddings, indexing, context construction, and evaluation.

• Familiarity with PostgreSQL and data modeling, along with exposure to Redis, pgvector, vector databases, or hybrid search technologies.

• Demonstrated use of AI coding agents such as Cursor, Claude Code, Codex, or similar tools as a routine part of software engineering activities.

• Understanding of prompting, structured outputs, tool schemas, context management, retries, fallbacks, rate limits, caching, and streaming responses.

• Ability to utilize experiments and evaluations to assess approaches based on measurable outcomes.

• Knowledge 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 tools.

• Familiarity with Semantic Kernel, Microsoft Agent Framework, LangGraph, PydanticAI, or equivalent agent frameworks.

• Experience in building or utilizing Model Context Protocol tools and servers, or working with model gateways such as LiteLLM.

• Knowledge of Azure AI Search, pgvector, Chroma, Kubernetes, Azure, OCI, or similar production AI platform technologies.

• Experience contributing to customer-facing AI products, open weight model inference, AI safety controls, automated evaluations, or high-scale backend platforms.

🏝️ Benefits

• No benefits, perks, or compensation extras are specified in the posting.