AI Software Engineer (Agentic Workflows & Production Systems)

Exadel Inc (Website)

4h•CA$25 - CA$30•Remote

About The Position

This role involves designing and implementing production-ready AI workflows, optimizing Retrieval-Augmented Generation (RAG) pipelines, and developing scalable services. The engineer will integrate AI services into CI/CD pipelines, implement monitoring and logging, and design prompt engineering strategies. A key aspect is implementing evaluation frameworks, hallucination detection, and guardrails to ensure model quality and reliability. The position requires collaboration with cross-functional teams to deliver secure and scalable AI solutions for complex business problems.

Requirements

Hands-on experience designing and deploying production-grade agentic AI workflows.
Strong Python development experience; Java experience is highly desirable.
Deep expertise with Retrieval-Augmented Generation (RAG), vector databases, embeddings, hybrid search, and retrieval optimization.
Experience with LangGraph, LangChain, Model Context Protocol (MCP), FastAPI, and asynchronous Python development.
Strong understanding of prompt engineering, context window optimization, and LLM orchestration patterns.
Experience implementing AI solutions within enterprise CI/CD pipelines and production environments.
Knowledge of software engineering best practices, including testing, monitoring, observability, documentation, and production support.
AI & Technology Stack: Python, Java, LangGraph, LangChain, MCP (Model Context Protocol), FastAPI, Async Python, Pinecone, Weaviate, Cloudflare Vectorize, pgvector, MongoDB, Elasticsearch, Kafka, OpenTelemetry, Grafana, Tempo, Mem0, GitHub Actions, Jenkins, Galileo Evaluation Framework, Guardrails, LLM-as-a-Judge, Claude Code, and AMP.
Strong analytical and problem-solving abilities.
Excellent communication and collaboration skills.
Ability to balance experimentation with production-grade engineering practices.
Strong ownership mindset with attention to scalability, reliability, and maintainability.
Passion for continuous learning within the rapidly evolving AI landscape.

Nice To Haves

Experience within Financial Services, Risk Management, or Compliance domains.
Experience building enterprise AI platforms and intelligent automation solutions.
Familiarity with large-scale knowledge management and semantic search systems.
Experience designing AI evaluation pipelines and model governance frameworks.

Responsibilities

Design and implement production-ready agentic AI workflows using modern multi-agent and single-agent architectures.
Build, optimize, and maintain Retrieval-Augmented Generation (RAG) pipelines utilizing vector databases, hybrid search, retrieval re-ranking, and prompt optimization.
Develop scalable Python and Java services following enterprise software engineering best practices.
Integrate AI services into CI/CD pipelines while implementing monitoring, logging, and observability for production environments.
Design prompt engineering strategies that optimize context windows, model performance, and operational costs.
Implement evaluation frameworks, hallucination detection, guardrails, and continuous feedback loops to improve model quality and reliability.
Collaborate with cross-functional engineering teams to deliver secure, scalable AI solutions that solve complex business problems.