AI/ML EngineerJob Description
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Job Description Sample
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Job Title: AI/ML Engineer
Location: Remote/Hybrid
Type: Full-time
About the Role:
We are seeking an experienced AI/ML Engineer to drive innovation in our machine learning initiatives. The ideal candidate will design and implement scalable ML models while collaborating with cross-functional teams to deliver AI-powered solutions. This role offers the opportunity to work on cutting-edge projects that directly impact our product development and business outcomes.
Key Responsibilities:
- Design and develop machine learning models and algorithms
- Implement and maintain scalable ML pipelines
- Optimize existing ML models for improved performance
- Collaborate with data scientists and software engineers
- Research and implement new AI/ML technologies
- Create documentation for ML systems and processes
- Monitor and evaluate model performance metrics
- Troubleshoot and debug ML applications
Perks:
- Flexible remote work options
- Comprehensive healthcare coverage
- Professional development budget
- Regular team events and workshops
AI/ML Engineer Responsibilities
Hiring a ai/ml engineer? Here's what you can expect them to handle:
- Lead the development of machine learning models from conception to deployment
- Optimize model performance and scalability for production environments
- Collaborate with data scientists to implement effective ML solutions
- Design and maintain ML infrastructure and pipelines
- Conduct research on latest AI/ML technologies and methodologies
- Implement data preprocessing and feature engineering techniques
- Develop APIs and services for model deployment
- Create and maintain technical documentation for ML systems
Qualifications to Be a AI/ML Engineer
Here's what a solid candidate typically brings to the table:
Master's degree in Computer Science, AI, or related field5+ years of experience in machine learning developmentStrong programming skills in Python and related ML frameworksProven track record of deploying ML models to productionExperience with deep learning architecturesAI/ML Engineer Prerequisites
Before you even think of hiring, make sure your candidates have:
Knowledge of distributed computing systemsFamiliarity with cloud platforms (AWS/Azure/GCP)Understanding of data structures and algorithmsExperience with version control systemsStrong problem-solving abilitiesAI/ML Engineer Hard Skills
The “must-haves” on every recruiter's checklist:
Programming Languages: Python, Java, RMachine Learning: TensorFlow, PyTorch, Scikit-learnDeep Learning: Neural Networks, CNN, RNN, TransformersData Processing: Pandas, NumPy, Apache SparkCloud Computing: AWS SageMaker, Azure ML, Google AI PlatformMLOps: MLflow, Kubeflow, DVCDatabase Systems: SQL, MongoDB, RedisBig Data: Hadoop, Spark, KafkaAI/ML Engineer Soft Skills
Tech skills get them in the door—soft skills help them stick around.
Clear communication of complex technical conceptsProblem-solving and analytical thinkingProject management and organizationTeam collaboration and leadershipAdaptability to new technologiesTime management and prioritizationAttention to detailStrategic thinking and planningAI/ML Engineer Salary by Experience Level
Entry Level (0-2 Years)
$95k – $130k
Common Titles
Junior AI/ML Engineer, Associate AI/ML Engineer
Mid Level (2-5 Years)
$130k – $180k
Common Titles
AI/ML Engineer, AI/ML Engineer II
Senior Level (5+ Years)
$180k – $300k
Common Titles
Senior AI/ML Engineer, Lead AI/ML Engineer
Frequently Asked QuestionsAbout AI/ML Engineer Hiring
AI/ML Engineers focus on implementing and deploying ML models at scale, while Data Scientists concentrate on statistical analysis and model development. Engineers need stronger software engineering skills and production deployment experience.
Use a combination of system design interviews focusing on ML architecture, coding tests with real ML problems, and portfolio reviews of deployed projects. Ask for specific examples of production ML systems they've built.
For most business applications, prioritize MLOps experience as it's crucial for production deployment. Deep learning expertise becomes more important for specialized applications like computer vision or NLP.
Watch for candidates who can't explain their models' business impact, lack production deployment experience, or have no experience with ML monitoring and maintenance. Also be wary of those who can't discuss model performance metrics.
Start with 2-3 engineers for most projects - one senior lead and 1-2 mid-level engineers. Scale the team based on project complexity and the number of ML models in production.
Tools and Programs AI/ML Engineer Use
Here's what their digital toolbox might look like:
ML Frameworks
TensorFlow•PyTorch
Cloud Platforms
AWS•Azure
Version Control
Git•GitHub
Containers
Docker•Kubernetes
IDEs
PyCharm•Jupyter
CI/CD
Jenkins•CircleCI
Monitoring
Prometheus•Grafana
Visualization
Tableau•PowerBI
Job Description Examples
.NET Architect.NET DeveloperAI/ML EngineerAndroid DeveloperAngular DeveloperApp DeveloperAzure ArchitectAzure EngineerBackend ArchitectBackend EngineerBackend Engineer InternBusiness Analyst InternCloud ArchitectCloud EngineerComputer EngineerComputer TechnicianCyber Security AnalystCyber Security ArchitectCyber Security InternCyber Security ManagerData ArchitectData EngineerData Engineering InternData Engineering ManagerDatabase AdministratorDatabase ArchitectDesktop Support ManagerDesktop Support TechnicianDevOps EngineerDevOps Engineer InternDevOps ManagerDirector Of Cyber SecurityDirector Of ITDirector Of Software EngineeringFrontend ArchitectFrontend Engineer InternFull Stack ArchitectFull Stack EngineerFull Stack Engineer InternGame DeveloperIntegration AnalystIntegration ArchitectIT InternIT Manager
.NET Architect.NET DeveloperAI/ML EngineerAndroid DeveloperAngular DeveloperApp DeveloperAzure ArchitectAzure EngineerBackend ArchitectBackend EngineerBackend Engineer Intern
Business Analyst InternCloud ArchitectCloud EngineerComputer EngineerComputer TechnicianCyber Security AnalystCyber Security ArchitectCyber Security InternCyber Security ManagerData ArchitectData Engineer
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