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ML Engineer
ITRex GroupML Engineer creating scalable ML-driven features for a live-streaming platform with extensive user engagement. Collaborating across teams to enhance user experience and performance.
Tech Stack
Tools & technologiesAWSCloudDockerGoogle Cloud PlatformNoSQLNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow
About the role
Key responsibilities & impact- Design, develop, and deploy machine learning models for predictive analytics, classification, NLP, and other data-driven tasks
- Implement data pipelines for ingestion, preprocessing, feature engineering, and model training
- Containerize ML models and applications using Docker for scalable and reproducible deployments
- Deploy and maintain ML solutions in cloud environments (AWS/Snowflake)
- Optimize model performance, latency, and resource utilization for real-time or batch inference
- Monitor and troubleshoot ML models in production, ensuring reliability and robustness
- Сollaborate with Product, Engineering, Data, and business stakeholders to define project requirements and integrate ML models into production systems
- Conduct rigorous model evaluation using appropriate metrics to ensure performance and fairness
- Assess whether machine learning is necessary for a given problem or if alternative rule-based/statistical approaches are more appropriate
Requirements
What you’ll need- 4+ years of experience as a Software Engineer, with at least 3 years in an ML Engineer role
- Strong understanding of machine learning techniques, including supervised & unsupervised learning, NLP, deep learning fundamentals, and model evaluation
- Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-Learn, Pandas, and NumPy
- Hands-on experience in containerizing ML applications using Docker for scalable deployment
- Practical experience with at least one cloud provider (AWS, GCP)
- Strong background in working with large datasets, SQL/NoSQL databases
- Ability to decompose complex problems into well-structured ML tasks
- Skilled at assessing whether ML is the best approach or if a simpler solution (e.g., heuristic rules, statistical methods) would be more effective
- Expertise in debugging, optimizing, and enhancing models for performance, efficiency, and interpretability
- Experience maintaining ML workflows to ensure reproducibility, scalability, and operational efficiency
- Excellent communication skills, capable of explaining ML concepts to both technical peers and non-technical stakeholders
- Collaborative, product-focused approach within Agile, cross-functional environments
- Proactive mindset with a strong sense of ownership with the ability to lead ML tasks end-to-end, from discovery and experimentation to production deployment and support
- Continuous learning mindset with awareness of current ML/AI trends, tools, and best practices
- English proficiency at an Upper-Intermediate level or above
Benefits
Comp & perks- Remote flexibility: Work where and how you work best - we trust you to deliver
- Fair compensation: Competitive salary + benefits that matter (medical, learning)
- Ownership opportunities: See a problem worth solving? Own it. We back smart risks over bureaucratic safety
- AI enhancement: We leverage AI to make you faster and stronger - complementing your abilities, not replacing them
- Learning investment: English classes, professional development
- Career progression: Real paths up, not just sideways shuffling
- Responsive teammates: No ignored Slacks, no "not my problem" attitudes
- Supportive culture: When you're stuck, people help. When things break, we fix them together
- Human connections: Regular meetups, tech talks, and actual relationships beyond work
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningpredictive analyticsclassificationnatural language processingdata pipelinesfeature engineeringmodel trainingmodel evaluationdebuggingoptimizing
Soft Skills
communicationcollaborationproblem decompositionproactive mindsetownershipcontinuous learningproduct-focused approachexplanation of ML conceptscross-functional teamworkadaptability