À propos de ce poste AI Engineer Intern chez Shyftlabs
About ShyftLabs
At ShyftLabs, we live and breathe data. Since 2020, we’ve been helping Fortune 500 companies unlock growth through innovative digital solutions that drive real business impact. With teams across Canada, the U.S., and India, we’re growing quickly and looking for curious, motivated individuals who are excited to learn, build, and solve meaningful problems with technology.
About Continuum
Continuum is an enterprise AI agent execution and control platform designed to help teams build, run, and deploy reliable agentic applications. It provides agent orchestration, multi-model routing, persistent memory, tool integration, durable workflows, governance, guardrails, evaluation, and observability.
We are looking for an AI Engineer Intern interested in building production-ready AI agents and applications. You will contribute to the Continuum platform while also using it to build agentic applications for real enterprise use cases.
This is a hands-on engineering role. You will work across agent orchestration, memory systems, model optimization, retrieval, tools, guardrails, evaluation, and application development.
What You'll Be Doing
Build and improve agent orchestration and multi-agent workflows.
Develop agentic applications for enterprise use cases using Continuum.
Work with different commercial and open-source models, including OpenAI, Anthropic, Gemini, Llama, Qwen, Mistral, and others.
Improve intelligent model routing based on task complexity, quality, latency, and cost.
Build persistent memory and state-management capabilities for long-running agent workflows.
Develop tool-calling functionality and integrations with APIs, databases, and enterprise systems.
Work with MCP servers and function tools to connect agents with external services.
Design context-engineering and retrieval pipelines using vector and graph databases.
Implement AI guardrails for safety, security, access control, data privacy, and policy enforcement.
Create evaluation pipelines to measure accuracy, groundedness, hallucination, tool usage, workflow completion, latency, and cost.
Improve the observability and traceability of agent decisions, tool calls, and workflow execution.
Optimize prompts, model usage, token consumption, response time, and infrastructure costs.
Build APIs, backend services, and user-facing prototypes for agentic applications.
Write clean, reusable, well-tested, and documented Python code.
Contribute to Continuum’s open-source codebase, examples, documentation, and developer experience.
What We Are Looking For
Currently pursuing or recently completed a degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
Strong programming skills in Python.
Good understanding of machine learning, natural language processing, and LLM fundamentals.
Experience building at least one LLM-powered or agentic application.
Familiarity with prompt engineering, embeddings, RAG, tool calling, and structured outputs.
Experience working with APIs, Git, databases, and standard software engineering practices.
Ability to research complex technical problems, experiment with different approaches, and clearly communicate results.
Strong interest in building reliable AI systems, not just basic LLM demos
Nice to Have
Experience with agent frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI, or similar technologies.
Familiarity with OpenAI, Anthropic, Gemini, AWS Bedrock, or open-source models.
Experience with vector databases such as Milvus, Pinecone, Weaviate, or Chroma.
Experience with graph databases such as Neo4j.
Knowledge of PostgreSQL, Redis, Docker, Kubernetes, or cloud infrastructure.
Familiarity with AI observability and evaluation tools such as Langfuse.
Understanding of multi-tenancy, identity management, authorization, or enterprise security.
Experience with model routing, inference optimization, prompt compression, or cost optimization.
Contributions to open-source AI projects, research, hackathons, or technically strong personal projects.
