Key Responsibilities
- Architect & Build: Develop, fine-tune, and optimize LLMs, multimodal models, and GenAI pipelines tailored for specific business use cases.
- Agentic Frameworks: Design and implement agentic workflows and multi-agent systems using frameworks like LangGraph, LangChain, or LlamaIndex.
- RAG & Vector Ops: Implement Retrieval-Augmented Generation (RAG) using vector databases, embeddings, and advanced prompt-engineering strategies.
- Data Engineering: Build scalable AI/ML systems and data pipelines using Azure Databricks, ADF, and PySpark.
- Deployment & MLOps: Deploy AI agents and models into production using Azure AI Foundry, ensuring adherence to enterprise best practices for security and scalability.
- Evaluation: Conduct benchmarking, A/B testing, and rigorous model evaluation to ensure performance and accuracy in production environments.
- Collaborate: Partner with product and domain teams to translate complex business problems into viable AI-powered solutions.
Technical Skills & QualificationsCore Azure & Data Engineering (Primary)
- Azure Ecosystem: Extensive experience with Azure AI Foundry, Azure Data Factory (ADF), and Azure Databricks.
- Big Data: Strong proficiency in PySpark for data processing and pipeline management.
- Production Deployment: Proven track record of deploying AI agents on Azure with a focus on production-grade reliability and monitoring.
Generative AI & Machine Learning
- GenAI Proficiency: At least 2 year of hands-on experience with LLMs (GPT, Llama, Claude, Mistral) and transformers.
- Agentic Workflows: Practical experience with LangGraph, LangChain, or LlamaIndex to build autonomous or semi-autonomous AI agents.
- Advanced Techniques: Experience in fine-tuning, prompt engineering, and working with multimodal AI (Vision + Language).
- Programming: Advanced Python scripting skills and familiarity with ML frameworks (PyTorch/TensorFlow).
MLOps & Infrastructure
- Containerization: Familiarity with Docker and Kubernetes for model serving.
- Lifecycle Management: Knowledge of MLOps tools such as MLflow or Kubeflow to monitor and troubleshoot AI systems.
Preferred Experience
- Direct experience solving high-impact business problems with AI solutions.
- Strong understanding of the end-to-end AI lifecycle, from data ingestion to real-time inference monitoring.
Location: Mississauga, ON (3 Days Onsite)
