Senior AI Engineer, IT Solutions 1

Celestica International LP•Toronto, ON

4d•CA$123,200 - CA$173,200•Hybrid

About The Position

We are seeking a highly motivated and technically proficient AI Engineer to join our growing Data & Analytics team. In this role, you will be a key liaison between business stakeholders and the technical AI team, translating complex business challenges into scalable artificial intelligence and machine learning solutions. You will be responsible for defining technical requirements, designing AI architectures (including Generative AI and RAG patterns), and collaborating with the Data Center of Excellence to deliver high-quality, production-ready AI tools that drive innovation and operational efficiency across the organization.

Requirements

11+ years of experience in Information Technology, Software Engineering, or Data Science, with a significant focus on AI/ML development.
Strong understanding of Generative AI landscapes, including LLMs, prompt engineering, and vector databases (e.g., Pinecone, Weaviate, Milvus).
Proven ability to architect end-to-end AI solutions from discovery to production deployment.
Excellent communication skills, with the ability to explain complex technical AI concepts to non-technical business leaders.
Advanced proficiency in Python and relevant libraries (NumPy, Pandas, PyTorch, or TensorFlow).
Experience with Cloud AI Services (Azure AI Studio, AWS Bedrock, or Google Vertex AI [Preferred]).
Knowledge of SQL and advanced data modeling for structured and unstructured data.
Familiarity with MLOps tools (MLflow, Kubeflow) and containerization (Docker, Kubernetes).
Experience working in an Agile/Scrum development environment.
Knowledge of AI security frameworks and responsible AI practices (e.g., OWASP for LLMs, MCP).
Industry experience in manufacturing or a related industrial sector.
11+ years of progressive experience in technical roles, with at least 3-5 years specifically focused on AI/ML engineering or architecture.
Proven track record of delivering production-grade AI applications.
AI-related certifications (e.g., Azure AI Engineer Associate, AWS Machine Learning Specialty) are highly preferred.

Nice To Haves

Experience with Cloud AI Services (Azure AI Studio, AWS Bedrock, or Google Vertex AI)
Knowledge of SQL and advanced data modeling for structured and unstructured data.
Familiarity with MLOps tools (MLflow, Kubeflow) and containerization (Docker, Kubernetes).
Experience working in an Agile/Scrum development environment.
Knowledge of AI security frameworks and responsible AI practices (e.g., OWASP for LLMs, MCP).
Industry experience in manufacturing or a related industrial sector.
AI-related certifications (e.g., Azure AI Engineer Associate, AWS Machine Learning Specialty) are highly preferred.

Responsibilities

Elicit and document technical requirements for AI and Machine Learning projects through workshops and deep dives with stakeholders across various departments.
Define the technical feasibility of proposed AI use cases, identifying appropriate model architectures (LLMs, SLMs, or traditional ML) and success metrics (Accuracy, F1-score, Perplexity, etc.).
Analyze existing business processes to identify automation opportunities and areas where Generative AI can provide a competitive advantage.
Work with stakeholders to identify and prepare high-quality datasets for model training, fine-tuning, and grounding.
Design and implement data ingestion pipelines for vector databases, ensuring data integrity and optimal embedding strategies for Retrieval-Augmented Generation (RAG).
Collaborate with data engineers to ensure scalable, secure, and compliant data flows between enterprise systems and AI models.
Develop, test, and refine AI prompts and orchestration workflows using frameworks like LangChain, LlamaIndex, or Semantic Kernel.
Evaluate and select appropriate foundation models (OpenAI, Anthropic, Llama, etc.) based on performance, cost, and latency requirements.
Translate business logic into technical specifications for API integrations, model endpoints, and user interfaces.
Implement MLOps best practices to ensure the continuous integration and deployment (CI/CD) of AI models.
Establish monitoring frameworks to track model performance, "drift," and hallucination rates in production environments.
Ensure AI solutions adhere to corporate data governance, security, and ethical AI principles.