Machine Learning Engineer

Compunnel

$120K — $145K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years of experience in machine learning and data science
  • Strong proficiency in data wrangling and preprocessing techniques
  • Experience with ML experimentation and baseline model development
  • Ability to fine-tune models for optimal performance
  • Familiarity with performance metrics and validation techniques
  • Experience collaborating with cross-functional teams
  • Mentorship experience with junior data scientists

Responsibilities

  • Identify and communicate AI/ML business problems effectively
  • Conduct data wrangling relevant to defined business problems
  • Execute ML experiments and develop baseline models
  • Optimize models and ensure they meet acceptance criteria
  • Document findings and communicate them to stakeholders
  • Collaborate with product teams for model deployment and new releases
  • Set OKRs for self and team, providing constructive feedback

Benefits

  • Opportunities for professional development and mentorship
  • Collaborative work environment with cross-functional teams
  • Access to the latest AI/ML tools and technologies
  • Possibility to lead innovative product releases
  • Dynamic project landscape allowing for rapid prototyping
Full Job Description
JOB SUMMARY Design and develop ML solutions, that will enable intelligent experiences and provide value. Formulate AI scope by working collaboratively with business, technology, and product teams to understand the product objectives with some guidance from Specialist 1. Key Responsibilities 1. Identify and formulate business problem to AI / ML related problems; identifying and communicating AI Scope with stake holders 2. Executes relevant data wrangling activities related to the problem 3. Conduct ML experiments to understand the feasibility; building baseline models to solve the business problem 4. Fine tune the model for optimum performance 5. Test Models internally per acceptance criteria from business 6. Identify areas and techniques to optimize the model based on test results 7. Document relevant artefacts for communicating with the business 8. Work with data scientists to deploy the models. 9. Work with product teams in planning and execution of new product releases. 10. Set OKRs and success steps for self/ team and provide feedback of goals to team members 11. Identify metrics for validating the models, with the ability to communicate the same in business terms to the product teams. 12. Keep track of trends and do rapid prototyping to understand the feasibility of using it in existing solutions 13. Visualise and build more complex models / solutions which address scalable solutions. Work with product teams in planning and execution of new product releases 14. Mentor junior data scientists in their delivery solutions 15. Work with product design and product management teams identifying design interventions of ML Models with guidance from Specialist ML Engineer I

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