Role: AI Engineer
Location: Mississauga, ON- Canada
Position Type: Fulltime
Years of exp - Minimum 8+ years
Mandate
:GenAI , Python , LLM (Google Gemini, OpenAI models, Anthropic Claude, Mistral, Llama),MLOps, Retrieval-Augmented Generation (RAG
)
Job Descripti
- on8 years of relevant experience in Apps Development or systems analysis ro
le
Core AI/ML Foundatio
- ns: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 (LLM
s).
Generative AI & LLM Expert
- ise:Extensive hands-on experience with leading LLMs such as Google Gemini, OpenAI models, Anthropic Claude, Mistral, Llama, and various other open-source L
- LMs.Critical: Deep working knowledge and hands-on experience with Retrieval-Augmented Generation (RAG) pipelines, including advanced RAG techniques and their detailed implementat
- ion.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 templa
- tes.Hands-on experience with agentic framework-based use case implementat
- ion.Working knowledge of Guardrails and methodologies for assessing the performance and safety of GenAI featu
res.
Programming & Data Enginee
- ring: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 LlamaI
- ndex.Proficiency in integrating generative AI with enterprise applications using APIs, knowledge graphs, and orchestration t
- ools.Hands-on experience with various vector databases (e.g., PG Vector, Pinecone, Mongo Atlas, Neo4j) for efficient data storage and retri
- eval.Experience in dealing with large amounts of unstructured data and designing solutions for high-throughput proces
sing.
Deployment &
- MLOps:Critical: Hands-on experience deploying GenAI-based models to production environ
- ments.Strong understanding and practical experience with MLOps principles, model evaluation, and establishing robust deployment pipe
- lines.Strong expertise in CI/CD principles and tools (e.g., Jenkins, GitLab CI, Azure DevOps, ArgoCD) for automated builds, testing, and deploy
- ments.Cloud & Containeriz
- ation:Proven experience with container orchestration platforms like OpenShift or Kubernetes for deploying, managing, and scaling containerized applications in a cloud-native enviro
nment.
