Job Description
Location: Remote / Hybrid (Kyiv)
Employment: Full-time
Time Zone: Partial overlap with US working hours
About the Role
We are looking for a Senior Machine Learning Engineer to join our client's team and build scalable, production-grade machine learning systems that power real-time personalization and intelligent decision-making.
In this role, you will own the complete machine learning lifecycle—from feature engineering and model development to deployment, monitoring, and continuous optimization. You'll work on large-scale ML solutions that directly impact customer engagement and business outcomes while collaborating closely with product and engineering teams.
If you enjoy solving complex engineering challenges, building reliable ML infrastructure, and delivering high-impact AI solutions, we'd love to hear from you.
Key Responsibilities
Design, develop, and deploy machine learning models for personalization, ranking, recommendation, and customer engagement.
Build scalable data pipelines that transform behavioral, demographic, and contextual data into high-quality online and offline features.
Develop and maintain low-latency prediction APIs and real-time decision services.
Implement experimentation frameworks, including A/B testing and exploration/exploitation strategies.
Build and maintain end-to-end MLOps pipelines, including automated model training, CI/CD, feature and model versioning, and deployment automation.
Monitor production ML systems by tracking data quality, model performance, drift, and business metrics, triggering retraining when required.
Create closed feedback loops that continuously improve model performance based on user behavior and business outcomes.
Collaborate with cross-functional teams to balance personalization, compliance requirements, and business objectives.
Requirements
5+ years of commercial experience in Machine Learning Engineering, ideally working with recommendation systems, personalization, ranking, or advertising platforms.
Strong programming skills in Python or Go
Advanced SQL skills and experience working with large-scale datasets.
Hands-on experience with modern ML frameworks such as TensorFlow, PyTorch, or similar.
Solid understanding of MLOps practices, including:
CI/CD for machine learning
Docker
Kubernetes
Airflow or Kubeflow
Model registries
ML monitoring and observability
Experience with cloud ML platforms such as Google Vertex AI, AWS SageMaker, or similar services.
Experience working with cloud data warehouses including BigQuery, Snowflake, or Amazon Redshift
Experience deploying real-time ML systems, feature stores, and event-driven architectures.
Understanding of multi-objective optimization and personalization trade-offs.
Comfortable working in a fast-paced, product-oriented environment with partial overlap with US business hours.
Strong English communication skills.
Nice to Have
Experience in MarTech, AdTech, CRM, or large-scale personalization platforms.
Knowledge of bandit algorithms, reinforcement learning, or causal inference.
Experience building ML systems serving millions of users.
Hands-on experience with Google Cloud Platform (GCP)
Familiarity with ML observability tools such as Prometheus, Grafana, Evidently, WhyLabs, or Great Expectations
What We Offer
Long-term, stable project with meaningful technical challenges.
Opportunity to work on high-impact AI and machine learning solutions.
Flexible working hours with remote or hybrid work options.
Modern office in Kyiv equipped with generator and battery backup.
Competitive compensation package.
Medical insurance.
Structured onboarding and supportive team environment.
Professional growth and continuous learning opportunities.
Team-building events and company activities.
The opportunity to work with a Top Employer (DOU 2025)
as we are looking for our custommer
Here's the version updated to make it clear that you are recruiting on behalf of your client
Senior Machine Learning Engineer
Location: Remote / Hybrid (Kyiv)
Employment: Full-time
Time Zone: Partial overlap with US working hours
About the Role
For our client, we are looking for a Senior Machine Learning Engineer to join a product-focused team building scalable machine learning solutions for real-time personalization and intelligent decision-making.
In this role, you will own the complete machine learning lifecycle—from feature engineering and model development to deployment, monitoring, and continuous optimization. You'll work on production-grade ML systems that directly impact customer engagement and business outcomes while collaborating closely with engineering and product teams.
This is an excellent opportunity for an experienced ML Engineer who enjoys solving complex engineering challenges and building reliable, high-performance machine learning platforms.
Key Responsibilities
Design, build, and deploy machine learning models for personalization, ranking, recommendation, and customer engagement.
Develop scalable data pipelines that transform behavioral, demographic, and contextual data into online and offline features.
Design and implement low-latency prediction APIs and real-time decision services.
Build experimentation frameworks, including A/B testing and exploration/exploitation strategies.
Develop and maintain end-to-end MLOps pipelines, including automated model training, CI/CD, model and feature versioning, and deployment automation.
Monitor production ML systems by tracking data quality, model performance, drift, and business metrics, triggering retraining when appropriate.
Create feedback loops that continuously improve models based on user behavior and business outcomes.
Collaborate closely with cross-functional teams to deliver scalable, compliant, and business-driven personalization solutions.
Requirements
5+ years of commercial experience in Machine Learning Engineering, preferably with recommendation systems, personalization, ranking, or advertising platforms.
Strong programming skills in Python or Go
Advanced SQL skills and experience working with large-scale datasets.
Hands-on experience with modern ML frameworks such as TensorFlow, PyTorch, or similar.
Strong understanding of MLOps practices, including:
CI/CD for ML
Docker
Kubernetes
Airflow or Kubeflow
Model registries
ML monitoring and observability
Experience with cloud ML platforms such as Vertex AI, AWS SageMaker, or similar.
Experience working with cloud data warehouses such as BigQuery, Snowflake, or Amazon Redshift
Experience deploying real-time ML systems, feature stores, and event-driven architectures.
Understanding of multi-objective optimization in personalization systems.
Comfortable working in a fast-paced product environment with partial overlap with US business hours.
Strong spoken and written English.
Nice to Have
Experience in MarTech, AdTech, CRM, or large-scale personalization platforms.
Knowledge of bandit algorithms, reinforcement learning, or causal inference.
Experience building ML systems serving millions of users.
Hands-on experience with Google Cloud Platform (GCP)
Familiarity with observability tools such as Prometheus, Grafana, Evidently, WhyLabs, or Great Expectations
What We Offer
Long-term B2B cooperation on an innovative product.
Interesting technical challenges with modern ML technologies.
Flexible working schedule with remote or hybrid work options.
Competitive compensation.
Professional growth and learning opportunities.
Supportive and collaborative international team.
Opportunity to contribute to high-impact machine learning solutions used at scale.
