Machine Learning Engineer

Akkodis — Toronto, CA
Published on

About the Role

Akkodis is seeking a Machine Learning Product Engineer for a contract position with a client in Toronto, ON (Hybrid). This role is designed for a candidate with experience in fine-tuning LLMs, Advanced RAG, and Agentic frameworks, as well as GenAI technologies and cloud technologies such as Azure or AWS.

Your Role in the Team

As part of the Investments Gen AI Team, you will pioneer transformative Generative AI innovations to tackle complex challenges with a forward-thinking approach. As an ML Engineer, your responsibilities will include:

  • Working in a diverse team and continuously learning new skills.
  • Identifying areas for innovation and exploring your ideas.
  • Designing, developing, and deploying AI/ML tools, and taking AI/ML solutions from proof-of-concept to production-ready states.

About the Candidate

An ideal candidate will demonstrate:

  • A deep understanding of machine learning fundamentals with practical application.
  • Experience with Generative AI technologies and techniques, including fine-tuning, RAG, vector stores, and Agentic frameworks.
  • Solid grasp of software development principles like design patterns and testing, with proficiency in Python.
  • Familiarity with cloud technologies (Azure/AWS) and development experience in Java Spring Boot and React JS is preferred.
  • Excellent communication skills to engage with both technical and non-technical team members.

Responsibilities Include

  • Engaging in hands-on coding, model development, and AI/ML experimentation focusing on production-ready solutions.
  • Collaborating with Digital Product Managers, Enterprise AI/ML teams, and Investment Business users to implement solutions across the Investments domain.

Essential Criteria to Apply

  • Minimum of 3 years of experience in AI/ML projects, specifically in productizing AI/ML solutions.

Desirable Criteria

  • Solid understanding of agile methodologies and experience with Kubernetes.
  • Further expertise in fine-tuning LLMs, Advanced RAG, and Agentic frameworks is greatly valued.
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