Data Scientist

VDart Inc; Mississauga; Hybrid; CAD 90,000 - 120,000; Full time

Job summary

VDart Inc. in Mississauga, ON is seeking a Data Scientist with strong GenAI and LLMOps experience to design and deploy scalable AI solutions.

You will work on production-grade ML models, RAG pipelines, and enterprise APIs while collaborating with cross-functional teams to move prototypes into robust deployments. The ideal candidate has expertise in Python, SQL, and modern ML frameworks, and can build high-performance APIs with FastAPI, Docker, and Kubernetes in a hybrid work environment.

Qualifications

• GenAI and LLMOps expertise for end-to-end AI solutions.
• Experience designing production-grade ML/GenAI systems across lifecycle.
• Strong Python and SQL for data-driven development.
• Ability to deploy scalable APIs using Docker/Kubernetes.

Responsibilities

• Design and implement end-to-end AIML solutions with GenAI and LLMOps.
• Develop and maintain scalable secure APIs for AI model deployment.
• Manage full ML model lifecycle: training, validation, deployment, monitoring.
• Collaborate with data scientists, engineers, and product teams to integrate AI into enterprise apps.
• Optimize AI workflows for high performance and production reliability.
• Stay updated on GenAI, LLMOps, MLOps to improve solutions.
• Troubleshoot AI model deployment and production issues.
• Document AI system architecture and deployment processes.

Skills

GenAI

LLMOps

Python

SQL

Tools

PyTorch

TensorFlow

Scikit-Learn

XGBoost

Docker

Kubernetes

FastAPI

LangChain

LangGraphs

Pgvector

Chroma

Spark

Iceberg

Job description

Job Title: Data Scientist

Location: Mississauga, ON Hybrid (2-3 Days onsite)
Mode: Fulltime

Job Summary

Seeking an AI Engineer skilled in GenAI LLMOps and MLOps to design develop and deploy scalable production grade machine learning and generative AI solutions

Job Description

Design develop and deploy production grade Machine Learning and Generative AI solutions Build intelligent systems including predictive ML models covering complete model lifecycle management Develop advanced applied Large Language Model LLM applications and Retrieval Augmented Generation RAG pipelines Automate complex workflows and enhance user experiences through AI driven solutions Collaborate with cross functional teams to transition AI models from prototypes to scalable secure and high performance enterprise APIs Utilize expertise in Python programming and SQL for solution development Implement Generative AI techniques including LLMs LangChain LangGraphs Claude Gemini prompt engineering and LLMOps Apply Machine Learning and Deep Learning frameworks such as PyTorch TensorFlow ScikitLearn and XGBoost Work with vector databases like Pgvector and Chroma for efficient data retrieval Leverage distributed processing tools including Apache Spark and Apache Iceberg Employ MLOps practices for deployment using Docker Kubernetes OpenShift and FastAPI Build high performance scalable APIs with Python FastAPI to expose AI capabilities required for solutions

Roles and Responsibilities

Design and implement end to end AIML solutions incorporating GenAI and LLMOps best practices

Develop and maintain scalable secure APIs for AI model deployment

Manage full ML model lifecycle including training validation deployment monitoring and retraining

Collaborate with data scientists engineers and product teams to integrate AI solutions into enterprise applications

Optimize AI workflows to ensure high performance and reliability in production environments

Stay updated with the latest advancements in Generative AI LLMOps and MLOps to continuously improve solutions

Troubleshoot and resolve issues related to AI model deployment and production systems

Document AI system architecture deployment processes and best practices

Mandatory Skills

GenAI - LLMOps, Python