Develop generative and predictive AI models (including NLP, computer vision, etc.)
Familiarity with cloud platforms (e.g., Azure, AWS, GCP) and big data tools (e.g., Databricks, PySpark) to develop AI solutions
Familiarity with intelligent autonomous agents for complex tasks and multimodal interactions
Familiarity with agentic workflows that utilize AI agents to automate tasks and improve operational efficiency
Deploy AI models into production environments, ensuring scalability, performance, and optimization
Monitor and troubleshoot deployed models and pipelines for optimal performance
Design and maintain data pipelines for efficient data collection, processing, and storage (e.g., data lakes, data warehouses)
Collaborate with cross-functional teams to align AI projects with business requirements and strategic goals
Willingness to contribute to and participate in developing and harvesting reusable assets and demos, sales pitches.
Communicate complex AI concepts and results to non-technical stakeholders.

Requirements

Bachelor’s or greater degree in Machine Learning, AI, or equivalent professional experience
Minimum of 1 year of professional experience in AI, application development, machine learning, or a similar role
Experience in model deployment, MLOps, model monitoring, and managing data/model drift
Proficiency in programming languages such as Python and SQL
Experience with predictive AI (e.g., regression, classification, clustering) and generative AI models (e.g., GPT, Claude LLM, Stable Diffusion)
Proficiency in URLs and API Endpoints, HTTP Requests, Authentication Methods, Response Types, JSON/REST, Parameters and Data Filtering, Error Handling, Debugging, Rate Limits, Tokens, Integration, and Documentation
Proficiency with cloud platforms (e.g., AWS, Azure) and big data tools (e.g., Databricks, PySpark)
Familiarity with AI frameworks such as LangChain and machine learning libraries like TensorFlow, PyTorch, and scikit-learn
Knowledge of deployment tools (e.g., Azure DevOps, Docker, AWS ECS/EKS/Fargate) and CI/CD pipelines (AWS CloudFormation, CodeDeploy)
Understanding of data engineering principles, including experience with SQL and NoSQL databases (e.g., MySQL, MongoDB, Redis)
Strong problem-solving and troubleshooting skills
Familiarity with generative AI techniques, such as retrieval-augmented generation (RAG) patterns
Experience with Graph database technology a plus (e.g. Neo4J, Ontotext)
Ability to collaborate effectively across teams
Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.

Benefits

Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade
Company paid holidays
Personal Days
Sick Leave
Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
Life and disability insurance
Employee assistance programs
Other benefits as provided by local policy and eligibility

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

generative AIpredictive AImachine learningNLPcomputer visionPythonSQLMLOpsdata engineeringmodel deployment

Soft Skills

problem-solvingtroubleshootingcollaborationcommunication

Certifications

Bachelor’s degree in Machine LearningBachelor’s degree in AI