Senior ML Engineer
⚲ Remote
20 000 - 30 000 PLN (B2B)
Wymagania
- Python
- ML
- Cloud platform
- AWS
- Docker
- Kubernetes
- Amazon EKS
- Infrastructure as Code
- MLOps
- CD pipelines
- GitHub Actions
- Git
- Testing
- Data engineering
- Data pipelines
- dbt
- Kafka
Opis stanowiska
O projekcie:
About the role
In this role, you will design, build, and operate scalable, production-grade ML systems. You’ll work at the intersection of Machine Learning Engineering, MLOps, and cloud-native infrastructure to enable the successful deployment and operation of AI solutions at scale for a leading UK grocery retailer.
You will collaborate closely with Data Scientists, Engineers, and Architects to transform ML prototypes into reliable, secure, and maintainable production systems. This role combines deep technical expertise with operational ownership, performance optimization, and engineering leadership.
SoftServe is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment regardless of race, color, religion, age, sex, nationality, disability, sexual orientation, gender identity and expression, veteran status, and other protected characteristics under applicable law. Let’s put your talents and experience in motion with SoftServe.
Wymagania:
- Expert-level Python skills and strong experience with modern ML frameworks and production-grade ML applications- Strong experience with cloud platforms such as AWS and/or Azure- Hands-on experience with containerization and orchestration technologies, including Docker and Kubernetes (EKS/AKS)- Experience with Infrastructure as Code tools, such as Terraform- Deep understanding of MLOps practices, including:- CI/CD pipelines (e.g., GitHub Actions)- Model versioning and experiment tracking (e.g., MLflow)- Workflow orchestration tools such as Airflow- Automated deployment, monitoring, and retraining workflows- Strong software engineering fundamentals, including Git, testing, code reviews, documentation, and maintainable coding practices- Experience implementing monitoring and observability for: Model performance tracking, Data and concept drift detection, System metrics, logging, and alerting- Solid understanding of data engineering fundamentals, including data pipelines, integration, transformation, and data quality processes (e.g., DBT, Kafka)
Codzienne zadania:
- Design, build, and maintain scalable, production-grade ML pipelines and infrastructure
- Lead end-to-end deployment of ML models from experimentation through production release and ongoing operation
- Translate Data Science prototypes into robust, maintainable, and production-ready ML services
- Make architectural and tooling decisions balancing scalability, performance, reliability, and maintainability
- Integrate ML solutions into enterprise applications and cloud-native environments
- Establish and maintain CI/CD pipelines for ML systems and infrastructure
- Implement model versioning, experiment tracking, monitoring, alerting, and automated retraining workflows
- Ensure high availability, reliability, observability, and operational stability of ML services in production
- Define and implement standards for monitoring model performance, drift detection, and system health
- Optimize inference pipelines for latency, throughput, scalability, and cost efficiency
About the role
In this role, you will design, build, and operate scalable, production-grade ML systems. You’ll work at the intersection of Machine Learning Engineering, MLOps, and cloud-native infrastructure to enable the successful deployment and operation of AI solutions at scale for a leading UK grocery retailer.
You will collaborate closely with Data Scientists, Engineers, and Architects to transform ML prototypes into reliable, secure, and maintainable production systems. This role combines deep technical expertise with operational ownership, performance optimization, and engineering leadership.
SoftServe is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment regardless of race, color, religion, age, sex, nationality, disability, sexual orientation, gender identity and expression, veteran status, and other protected characteristics under applicable law. Let’s put your talents and experience in motion with SoftServe.
Wymagania:
- Expert-level Python skills and strong experience with modern ML frameworks and production-grade ML applications- Strong experience with cloud platforms such as AWS and/or Azure- Hands-on experience with containerization and orchestration technologies, including Docker and Kubernetes (EKS/AKS)- Experience with Infrastructure as Code tools, such as Terraform- Deep understanding of MLOps practices, including:- CI/CD pipelines (e.g., GitHub Actions)- Model versioning and experiment tracking (e.g., MLflow)- Workflow orchestration tools such as Airflow- Automated deployment, monitoring, and retraining workflows- Strong software engineering fundamentals, including Git, testing, code reviews, documentation, and maintainable coding practices- Experience implementing monitoring and observability for: Model performance tracking, Data and concept drift detection, System metrics, logging, and alerting- Solid understanding of data engineering fundamentals, including data pipelines, integration, transformation, and data quality processes (e.g., DBT, Kafka)
Codzienne zadania:
- Design, build, and maintain scalable, production-grade ML pipelines and infrastructure
- Lead end-to-end deployment of ML models from experimentation through production release and ongoing operation
- Translate Data Science prototypes into robust, maintainable, and production-ready ML services
- Make architectural and tooling decisions balancing scalability, performance, reliability, and maintainability
- Integrate ML solutions into enterprise applications and cloud-native environments
- Establish and maintain CI/CD pipelines for ML systems and infrastructure
- Implement model versioning, experiment tracking, monitoring, alerting, and automated retraining workflows
- Ensure high availability, reliability, observability, and operational stability of ML services in production
- Define and implement standards for monitoring model performance, drift detection, and system health
- Optimize inference pipelines for latency, throughput, scalability, and cost efficiency
🔍 Dekoder Ogłoszenia
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design, build, and operate scalable, production-grade ML systems
Oczekuje się, że będziesz odpowiedzialny za cały cykl życia systemów ML, od projektowania po utrzymanie w produkcji.
🔴
work at the intersection of Machine Learning Engineering, MLOps, and cloud-native infrastructure
Rola wymaga szerokiego zakresu umiejętności, obejmujących zarówno ML, jak i inżynierię chmurową oraz praktyki operacyjne.
🔴
transform ML prototypes into reliable, secure, and maintainable production systems
Twoim zadaniem będzie przekształcanie eksperymentalnych modeli w stabilne i gotowe do produkcji rozwiązania.
🔴
combines deep technical expertise with operational ownership, performance optimization, and engineering leadership
Oczekuje się, że będziesz nie tylko ekspertem technicznym, ale także przejmiesz odpowiedzialność za działanie systemów i będziesz przewodzić zespołowi.
🔴
Expert-level Python skills
Oczekuje się bardzo zaawansowanej znajomości Pythona, wykraczającej poza podstawowe umiejętności programowania.