Position Description: Responsibilities: -Design, develop, and deploy autonomous and semi-autonomous AI agents to automate complex tasks across the software development lifecycle (SDLC). -Design and implement MCP (Model Context Protocol) servers to integrate large language models (LLM) with internal systems, development tools, and enterprise data sources. -Identify acceleration opportunities within the SDLC (code generation, code review, automated testing, documentation, deployment) and design tailored AI solutions. -Develop robust prompt engineering pipelines and agent chains (multi-agent orchestration) to solve concrete business problems. -Integrate generative AI solutions (LLM, RAG, agents) with existing Dev

Ops tools (CI/CD, ticketing systems, version control, etc.). -Collaborate with development, architecture, and operations teams to drive the adoption of AI practices within product teams. -Ensure the quality, reliability, and observability of AI agents in production (monitoring, tracing, output evaluation). -Actively monitor technological developments in LLMs, agent frameworks, and integration protocols (MCP, OpenAI function calling, tool use, etc.). -Document architectures, technical decisions, and usage guides for internal teams.

Required Qualifications: -Minimum 5 years of experience in software development, including at least 2 years focused on applied AI/ML.



-Hands-on experience developing AI agents using frameworks such as Lang

Chain, Lang

Graph, Auto

Gen, CrewAI, or equivalents. -Proficiency in setting up MCP servers and integrating LLMs via APIs (OpenAI, Anthropic, Azure OpenAI, etc.). -Solid Python experience and solid knowledge of software development best practices (testing, code review, versioning). -Experience with RAG (Retrieval-Augmented Generation) architectures and vector databases (Pinecone, Weaviate, pgvector, etc.). -Good understanding of the SDLC and Dev

Ops/MLOps practices (CI/CD, Docker, Kubernetes, Git). -Ability to communicate complex technical concepts to non-technical stakeholders. -Bilingual (French and English).

Additional Assets: -Experience with AI-powered developer tools: Git

Hub Copilot, Cursor, Codeium, or similar solutions. -Knowledge of agent integration protocols: MCP (Model Context Protocol), OpenAI Assistants API, tool use (Anthropic). -Experience in LLM evaluation and benchmarking (LLM-as-a-judge, RAGAS, Prompt

Flow, etc.). -AI/ML certification on a cloud platform (Azure AI Engineer, AWS ML Specialty, GCP Professional ML Engineer). -Experience in a consulting or client project delivery context.

Skills: Data computing & Mlops English French Large Language Model (LLM) Model Context Protocol Servers Retrieval-Augmented Gen.(RAG) Anthropic Claude Lang

Chain Lang

Graph OpenAI Python

📌 AI Expert developer (Montreal)
🏢 CGI
📍 Montreal