Service AI Engineer

Mississauga - Canada

Monthly Salary: Not Disclosed

Posted on: 6 hours ago

Vacancies: 1 Vacancy

Job Summary

The Service AI Engineer role based in Mississauga (hybrid) requires 6 10 years of experience in application development or systems analysis with strong expertise in AI/ML and Generative AI. The position focuses on designing building and deploying advanced AI solutions using large language models such as Google Gemini OpenAI models and Anthropic Claude with a critical emphasis on implementing and optimizing Retrieval-Augmented Generation (RAG) pipelines. The role involves hands-on development of LLM-based applications using platforms like Vertex AI and Hugging Face along with expertise in prompt engineering agentic frameworks and GenAI safety practices. Strong programming skills in Python and experience with AI/ML libraries vector databases APIs and large-scale unstructured data processing are essential. Additionally the candidate should have proven experience in deploying AI models into production environments supported by solid knowledge of MLOps model evaluation and scalable deployment pipelines.

Experience and Responsibilities:

6-10 years of relevant experience in Apps Development or systems analysis role
Core AI/ML Foundations:
- Strong foundational knowledge in GenAI Machine Learning (ML modeling) Data Science Statistics and AI fundamentals including Natural Language Processing (NLP) Neural Networks and Large Language Models (LLMs).
Generative AI & LLM Expertise:
- Extensive hands-on experience with leading LLMs such as Google Gemini OpenAI models Anthropic Claude Mistral Llama and various other open-source LLMs.
- Critical: Deep working knowledge and hands-on experience with Retrieval-Augmented Generation (RAG) pipelines including advanced RAG techniques and their detailed implementation.
- Proven ability to build tune and deploy LLM-based applications using platforms like Vertex AI Hugging Face etc.
- Expertise in developing robust prompt engineering strategies prompt tuning and creating reusable prompt templates.
- Hands-on experience with agentic framework-based use case implementation.
- Working knowledge of Guardrails and methodologies for assessing the performance and safety of GenAI features.
Programming & Data Engineering:
- Strong programming proficiency in Python is a must including extensive experience with libraries such as Pandas NumPy scikit-learn PyTorch TensorFlow Transformers FastAPI Seaborn LangChain and LlamaIndex.
- Proficiency in integrating generative AI with enterprise applications using APIs knowledge graphs and orchestration tools.
- Hands-on experience with various vector databases (e.g. PG Vector Pinecone Mongo Atlas Neo4j) for efficient data storage and retrieval.
- Experience in dealing with large amounts of unstructured data and designing solutions for high-throughput processing.
Deployment & MLOps:
- Critical: Hands-on experience deploying GenAI-based models to production environments.
- Strong understanding and practical experience with MLOps principles model evaluation and establishing robust deployment pipelines.

Experience and Responsibilities:

6-10 years of relevant experience in Apps Development or systems analysis role
Core AI/ML Foundations:
- Strong foundational knowledge in GenAI Machine Learning (ML modeling) Data Science Statistics and AI fundamentals including Natural Language Processing (NLP) Neural Networks and Large Language Models (LLMs).
Generative AI & LLM Expertise:
- Extensive hands-on experience with leading LLMs such as Google Gemini OpenAI models Anthropic Claude Mistral Llama and various other open-source LLMs.
- Critical: Deep working knowledge and hands-on experience with Retrieval-Augmented Generation (RAG) pipelines including advanced RAG techniques and their detailed implementation.
- Proven ability to build tune and deploy LLM-based applications using platforms like Vertex AI Hugging Face etc.
- Expertise in developing robust prompt engineering strategies prompt tuning and creating reusable prompt templates.
- Hands-on experience with agentic framework-based use case implementation.
- Working knowledge of Guardrails and methodologies for assessing the performance and safety of GenAI features.
Programming & Data Engineering:
- Strong programming proficiency in Python is a must including extensive experience with libraries such as Pandas NumPy scikit-learn PyTorch TensorFlow Transformers FastAPI Seaborn LangChain and LlamaIndex.
- Proficiency in integrating generative AI with enterprise applications using APIs knowledge graphs and orchestration tools.
- Hands-on experience with various vector databases (e.g. PG Vector Pinecone Mongo Atlas Neo4j) for efficient data storage and retrieval.
- Experience in dealing with large amounts of unstructured data and designing solutions for high-throughput processing.
Deployment & MLOps:
- Critical: Hands-on experience deploying GenAI-based models to production environments.
- Strong understanding and practical experience with MLOps principles model evaluation and establishing robust deployment pipelines.