Full Stack Engineer, Agentic AI

New

JobgetherAI software

Based in CanadaFull-TimeMiddle

Salary not disclosed

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Job Details

Have approximately 4–6 years of full-stack engineering experience delivering and maintaining production software.
Bring strong experience with TypeScript, React, and Node.js, plus proficiency in Python or Go.
Have practical experience building AI-powered applications and integrating LLMs into production workflows.
Have experience hosting, tuning, or managing LLM services, libraries, or tools, including model selection and production deployment.
Have experience designing and building agentic systems, including creating or integrating MCP servers and connecting models with external tools and services.
Have delivered AI-driven features such as chatbots, intelligent search, information extraction, document construction, or automated data transformation.
Be familiar with prompt design, structured experimentation, evaluation methodologies, and iterative improvement of model outputs.
Have experience fine-tuning large language models using open-source models or commercial services.
Be able to establish testing infrastructure for AI applications and validate agent behavior and non-deterministic outputs.
Have experience with at least one major cloud provider: AWS, Google Cloud Platform, or Microsoft Azure.
Have previous consulting, contracting, or client-facing engineering experience.
Be comfortable working autonomously, navigating ambiguity, and adapting to different client requirements and technical stacks.

Collaborate with clients to understand business objectives, technical environments, operational challenges, and product requirements.
Architect, build, test, and deploy scalable applications using TypeScript, React, Node.js, Python, and Go.
Design and implement AI-powered applications and agentic workflows, including integrations with MCP servers and LLM tools.
Host, configure, tune, and manage large language models and related services to meet performance, cost, and reliability requirements.
Develop AI features such as chatbots, information extraction, document generation, semantic search, and data normalization.
Establish testing infrastructure, experimentation workflows, and evaluation frameworks for model outputs and agent behavior.
Apply MLOps and LLMOps practices for monitoring, testing, deployment, observability, and operational reliability.
Design and implement scalable data pipelines, data lakes, and supporting infrastructure.
Evaluate architectural options and communicate technical trade-offs and risks to clients.
Collaborate with engineers, designers, and stakeholders, and incorporate relevant developments in AI, cloud technologies, and developer tooling.

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