Machine Learning Engineer (ai Agents)

Cresta Cresta · Vertical AI · AB, Canada, Canada · Remote · Engineering

Machine Learning Engineer focused on developing and deploying AI Agents, including proprietary LLMs, for contact center solutions. The role involves improving reasoning, planning, evaluation, and scaling AI systems for production environments, bridging the gap between research and real-world application.

What you'd actually do

  1. Design, develop, and deploy Cresta’s AI Agent solutions and proprietary models.
  2. Focus on practical AI challenges such as improving reasoning, planning capabilities, and evaluation in real-world scenarios.
  3. Collaborate with cross-functional teams including front-end and back-end software engineers to integrate AI Agents into Cresta’s customer solutions.
  4. Lead initiatives to scale AI systems for production environments, ensuring performance and reliability across use cases.
  5. Contribute to solving cutting-edge problems in AI and help define the future roadmap for Cresta’s AI Agents.

Skills

Required

  • 2+ years of hands-on industry experience with AI and machine learning
  • Experience working with LLMs in large-scale production environments
  • Solid knowledge of machine learning concepts and methods, especially those related to NLP, Generative AI, and working with LLMs
  • Practical knowledge of modern machine learning frameworks and technologies (e.g., PyTorch, Tensorflow, Hugging Face, NumPy)
  • Experience with distributed systems and cloud-based AI infrastructure
  • Strong problem-solving and strategic thinking abilities
  • Proven ability to lead cross-functional teams and work collaboratively to deliver innovative AI solutions in production

Nice to have

  • Bachelor’s or Master’s Degree in Computer Science, Mathematics, or a related field
  • Ability to mentor junior engineers and influence strategic decisions across the organization

What the JD emphasized

  • practical AI challenges
  • evaluation
  • reliability
  • scale AI systems
  • production environments
  • proprietary models

Other signals

  • Develop and deploy proprietary LLMs
  • Scale AI solutions
  • Evaluation and reliability
  • Bring research into practical, scalable applications