Artificial Intelligence Engineer

Role: AI Engineer

Location: Mississauga, ON- Canada

Position Type: Fulltime

Job Details

• 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 Expertis

• e:Extensive hands-on experience with leading LLMs such as Google Gemini, OpenAI models, Anthropic Claude, Mistral, Llama, and various other open-source LLM
• s.Critical: Deep working knowledge and hands-on experience with Retrieval-Augmented Generation (RAG) pipelines, including advanced RAG techniques and their detailed implementatio
• n.Proven ability to build, tune, and deploy LLM-based applications using platforms like Vertex AI, Hugging Face, et
• c.Expertise in developing robust prompt engineering strategies, prompt tuning, and creating reusable prompt template
• s.Hands-on experience with agentic framework-based use case implementatio
• n.Working knowledge of Guardrails and methodologies for assessing the performance and safety of GenAI feature

s.
Programming & Data Engineeri

• ng: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 LlamaInd
• ex.Proficiency in integrating generative AI with enterprise applications using APIs, knowledge graphs, and orchestration too
• ls.Hands-on experience with various vector databases (e.g., PG Vector, Pinecone, Mongo Atlas, Neo4j) for efficient data storage and retriev
• al.Experience in dealing with large amounts of unstructured data and designing solutions for high-throughput processi

ng.
Deployment & ML

• Ops:Critical: Hands-on experience deploying GenAI-based models to production environme
• nts.Strong understanding and practical experience with MLOps principles, model evaluation, and establishing robust deployment pipeli
• nes.Strong expertise in CI/CD principles and tools (e.g., Jenkins, GitLab CI, Azure DevOps, ArgoCD) for automated builds, testing, and deployme

nts.
Cloud & Containeriza

• tion:Proven experience with container orchestration platforms like OpenShift or Kubernetes for deploying, managing, and scaling containerized applications in a cloud-native environ

ment.
Soft S

• kills:Strong problem-solving abilities, excellent collaboration skills for working effectively with cross-functional teams, and the capability to work independently on complex, ambiguous pr