AI Engineer at Celestica translating complex business challenges into scalable AI solutions. Responsible for designing AI architectures and collaborating with the Data Center of Excellence.
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.
Requirements
11+ years of experience in Information Technology, Software Engineering, or Data Science
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.
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