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

oblems

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