Machine Learning Engineer

Job Summary
Qorsa Corp is a Canadian technology company pioneering quantum-proof AI solutions for governments, enterprises, and institutions. We build and integrate advanced machine learning with cryptographically agile systems built for the post-quantum era to help our clients better understand and protect their data.

Responsibilities

As a Machine Learning engineer, you’ll work on data ingestion and processing using various Natural Language Processing (NLP) tools. You will also work on building AI agents, fine-tunning LLMs, and building chatbots. Key responsibilities include:

• Design, build, and maintain AI Agents using Langchain and other similar tools.
• Fine-tune and test LLMs on various verticals explored by Qorsa.
• Develop and deploy applications using Docker and Kubernetes.
• Improve on our existing technologies including data processing, natural language pipelines, and building knowledge graphs

Experience

• Advanced proficiency in Python
• Experienced in using Langchain to build RAG applications.
• Understanding of key python machine learning libraries including Tensorflow, Pytorch, and HuggingFace
• Solid understanding of Docker, containerization, and Kubernetes
• Strong understanding of secure coding practices
• Proficiency with Git version control and collaborative development workflows
• Familiarity with SQL and Neo4j Cypher
• Experience with RESTful APIs, and asynchronous data flows.

Valuable Skills:

• Bachelor's Degree in Computer Science, Software Engineering, or related area
• Knowledge of Neo4j and graph database structures
• Hands-on experience deploying to and managing cloud-native environments, particularly GCP
• Solid understanding of algorithms and data structures
• Experience working with Linux environments

Job Types: Full-time, Permanent

Pay: From $100,000.00 per year

Benefits:

• Dental care
• Disability insurance
• Extended health care
• Life insurance
• On-site parking
• Paid time off
• Vision care

Education:

• Bachelor's Degree (preferred)

Work Location: In person