AI Engineer

Role: AI Engineer

Location:
Mississauga, ON
- Canada

Position Type:
Fulltime
Pay rate: CAD $125k per year

Job description:

• 8-10 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, Num Py, scikit-learn, PyTorch, Tensor Flow, Transformers, FastAPI, Seaborn, Lang Chain, and Llama Index.
  • 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.
  • Strong expertise in CI/CD principles and tools (e.g., Jenkins, Git Lab CI, Azure Dev Ops, ArgoCD) for automated builds, testing, and deployments.
• Cloud & Containerization:
  • Proven experience with container orchestration platforms like Open Shift or Kubernetes for deploying, managing, and scaling containerized applications in a cloud-native environment.
• Soft Skills:
  • Strong problem-solving abilities, excellent collaboration skills for working effectively with cross-functional teams, and the capability to work independently on complex, ambiguous problems