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AIT Global inc.

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Python Developer

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Summary

Senior Python developer (8-10 yrs) in Mississauga who builds, deploys, and operates GenAI/LLM applications day to day — RAG pipelines, prompt engineering, agentic frameworks, vector databases — using Python ML libraries, cloud platforms like Vertex AI, and MLOps/CI-CD tooling with Kubernetes or OpenShift.

Job Title: Python Developer
Location: Mississauga, ON

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, NumPy, scikit-learn, PyTorch, TensorFlow, Transformers, FastAPI, Seaborn, LangChain, and LlamaIndex.
  • 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, GitLab CI, Azure DevOps, ArgoCD) for automated builds, testing, and deployments.

Cloud & Containerization:

Proven experience with container orchestration platforms like OpenShift 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

Skills

See also

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