AI Engineer
Job Location:
Mississauga - Canada
Monthly Salary:
Posted on:
Vacancies:
Job Summary
Role: AI Engineer
Experience 8 years
Location: Mississauga ON (Hybrid)
Position Type: Fulltime
JD
8-10 years of relevant experience in Apps Development or systems analysis role
Core AI/ML Foundations:
o 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:
o Extensive hands-on experience with leading LLMs such as Google Gemini OpenAI models Anthropic Claude Mistral Llama and various other open-source LLMs.
o Critical: Deep working knowledge and hands-on experience with Retrieval-Augmented Generation (RAG) pipelines including advanced RAG techniques and their detailed implementation.
o Proven ability to build tune and deploy LLM-based applications using platforms like Vertex AI Hugging Face etc.
o Expertise in developing robust prompt engineering strategies prompt tuning and creating reusable prompt templates.
o Hands-on experience with agentic framework-based use case implementation.
o Working knowledge of Guardrails and methodologies for assessing the performance and safety of GenAI features.
Programming & Data Engineering:
o 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.
o Proficiency in integrating generative AI with enterprise applications using APIs knowledge graphs and orchestration tools.
o Hands-on experience with various vector databases (e.g. PG Vector Pinecone Mongo Atlas Neo4j) for efficient data storage and retrieval.
o Experience in dealing with large amounts of unstructured data and designing solutions for high-throughput processing.
Deployment & MLOps:
o Critical: Hands-on experience deploying GenAI-based models to production environments.
o Strong understanding and practical experience with MLOps principles model evaluation and establishing robust deployment pipelines.
o Strong expertise in CI/CD principles and tools (e.g. Jenkins GitLab CI Azure DevOps ArgoCD) for automated builds testing and deployments.
Cloud & Containerization:
o Proven experience with container orchestration platforms like OpenShift or Kubernetes for deploying managing and scaling containerized applications in a cloud-native environment.
Soft Skills:
o Strong problem-solving abilities excellent collaboration skills for working effectively with cross-functional teams and the capability to work independently on complex ambiguous problems.
