Senior Applied AI Engineer building production AI agents for Tango Analytics’ real estate technology platform. Designing retrieval, evaluation, safety, and human - review capabilities for customer workflows.
About the role
- 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.
Benefits
- comprehensive benefits package
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