{"id":720914,"url":"https://alion.io/job/shyftlabs-ai-engineer-intern","title":"AI Engineer Intern","company":{"id":685982,"name":"Shyftlabs","domain":"shyftlabs.io","url":"https://alion.io/company/shyftlabs","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":91,"open_postings":25,"ghost_share":0,"stale_share":0.36,"repost_share":0,"time_to_fill_p50_days":39,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"intern","employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":20,"max":30,"currency":"CAD","period":"hour","gross":null,"usd_annual":42000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"Gemini","optional":false},{"name":"Git","optional":false},{"name":"Hallucination","optional":false},{"name":"Llama","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Mistral","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Qwen","optional":false},{"name":"RAG","optional":false},{"name":"Structured Outputs","optional":false},{"name":"AutoGen","optional":true},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Chroma","optional":true},{"name":"CrewAI","optional":true},{"name":"Docker","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"Langfuse","optional":true},{"name":"LangGraph","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Milvus","optional":true},{"name":"Neo4j","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Redis","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-07-21T18:59:53Z","employer_posted_date":"2026-07-21","last_verified_at":"2026-10-29T13:40:27Z","board_verified":true,"closed_at":null,"days_open":99,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":99},"description":"About ShyftLabs\nAt 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.\nAbout Continuum\nContinuum 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.\nWe 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.\nThis is a hands-on engineering role. You will work across agent orchestration, memory systems, model optimization, retrieval, tools, guardrails, evaluation, and application development.\nWhat You'll Be Doing\nBuild and improve agent orchestration and multi-agent workflows.\n\nDevelop agentic applications for enterprise use cases using Continuum.\n\nWork with different commercial and open-source models, including OpenAI, Anthropic, Gemini, Llama, Qwen, Mistral, and others.\n\nImprove intelligent model routing based on task complexity, quality, latency, and cost.\n\nBuild persistent memory and state-management capabilities for long-running agent workflows.\n\nDevelop tool-calling functionality and integrations with APIs, databases, and enterprise systems.\n\nWork with MCP servers and function tools to connect agents with external services.\n\nDesign context-engineering and retrieval pipelines using vector and graph databases.\n\nImplement AI guardrails for safety, security, access control, data privacy, and policy enforcement.\n\nCreate evaluation pipelines to measure accuracy, groundedness, hallucination, tool usage, workflow completion, latency, and cost.\n\nImprove the observability and traceability of agent decisions, tool calls, and workflow execution.\n\nOptimize prompts, model usage, token consumption, response time, and infrastructure costs.\n\nBuild APIs, backend services, and user-facing prototypes for agentic applications.\n\nWrite clean, reusable, well-tested, and documented Python code.\n\nContribute to Continuum’s open-source codebase, examples, documentation, and developer experience.\n\nWhat We Are Looking For\nCurrently pursuing or recently completed a degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.\n\nStrong programming skills in Python.\n\nGood understanding of machine learning, natural language processing, and LLM fundamentals.\n\nExperience building at least one LLM-powered or agentic application.\n\nFamiliarity with prompt engineering, embeddings, RAG, tool calling, and structured outputs.\n\nExperience working with APIs, Git, databases, and standard software engineering practices.\n\nAbility to research complex technical problems, experiment with different approaches, and clearly communicate results.\n\nStrong interest in building reliable AI systems, not just basic LLM demos\n\nNice to Have\nExperience with agent frameworks such as LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI, or similar technologies.\n\nFamiliarity with OpenAI, Anthropic, Gemini, AWS Bedrock, or open-source models.\n\nExperience with vector databases such as Milvus, Pinecone, Weaviate, or Chroma.\n\nExperience with graph databases such as Neo4j.\n\nKnowledge of PostgreSQL, Redis, Docker, Kubernetes, or cloud infrastructure.\n\nFamiliarity with AI observability and evaluation tools such as Langfuse.\n\nUnderstanding of multi-tenancy, identity management, authorization, or enterprise security.\n\nExperience with model routing, inference optimization, prompt compression, or cost optimization.\n\nContributions to open-source AI projects, research, hackathons, or technically strong personal projects.\n\nHourly Pay\n$20 - 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