Welcome!

Our Growth is Creating Great Opportunities!

Our team is expanding, and we want to hire the most talented people we can.   Continued success depends on it!  Once you've had a chance to explore our current open positions, apply to the ones you feel suit you best and keep track of both your progress in the selection process, and new postings that might interest you!

Thanks for your interest in working on our team!

Adtran seeks an outstanding Machine Learning Ops Engineer II to work on the execution of ML/AI-driven Network Intelligence Solutions. You will collaborate with a highly skilled team of engineers, data scientists and subject matter experts to develop proof-of-concepts, explore AI-driven insights, and define next generation of data intelligence products. 

Responsibilities 

• Deploy, monitor, and manage machine learning models in production using MLOps best practices. 

• Automate model retraining, versioning, and deployment pipelines using CI/CD workflows. 

• Ensure scalability, reliability, and reproducibility of ML models in cloud or on-prem environments. 

• Design and implement end-to-end ML pipelines, including data ingestion, preprocessing, training, and inference. 

• Optimize and maintain data pipelines for feature engineering and model retraining. 

• Use tools like Airflow, Kubeflow, MLflow, or SageMaker Pipelines to orchestrate workflows. 

• Deploy ML workloads on AWS, leveraging services like SageMaker, Databricks, Kubernetes, and Vertex AI. 

• Optimize cloud resource utilization, cost management, and performance. 

• Implement containerization and orchestration using Docker, Kubernetes, and AWS Fargate. 

• Set up real-time model monitoring, logging, and alerting for performance tracking. 

• Implement model drift detection and automate retraining strategies. 

• Ensure models meet latency, throughput, and accuracy requirements. 

• Enforce data governance, security, and access control policies for ML models and data pipelines. 

• Ensure compliance with GDPR, HIPAA, and other industry regulations. 

• Implement authentication and encryption mechanisms for data and model security. 

• Work closely with data scientists, ML engineers, software engineers, and cloud architects. 

• Collaborate with DevOps teams to integrate ML workloads into broader CI/CD pipelines. 

• Participate in Agile workflows (sprint planning, stand-ups, retrospectives). 

Basic Qualifications 

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field. 

• 4+ years of experience in MLOps, DevOps for ML, or Cloud-based ML Engineering. 

• Strong programming skills in Python and Bash (knowledge of Go or Rust is a plus). 

• Hands-on experience with CI/CD tools (Jenkins, GitLab CI, ArgoCD). 

• Experience with containerization (Docker) and orchestration (Kubernetes, Kubeflow, MLflow). 

• Strong knowledge of cloud platforms (AWS, Azure, GCP) and ML services (SageMaker, Vertex AI). 

• Familiarity with monitoring and logging tools (Prometheus, Grafana, ELK Stack). 

• Understanding of machine learning workflows, model lifecycle management, and data pipelines. 

• B2+ English proficiency, with strong documentation and communication skills. 

Preferred Qualifications 

• Experience with Apache Airflow, Databricks, Kafka, or Spark for ML pipeline orchestration. 

• Hands-on experience with Feature Stores (Feast, AWS Feature Store). 

• Experience with distributed training and model serving frameworks (TensorFlow Serving, Triton Inference Server). 

• Knowledge of MLOps best practices, including model lineage tracking and automated retraining. 

• Security and compliance experience (IAM policies, RBAC, SOC2 compliance). 

• Experience working in Agile teams and across multi-cloud or hybrid environments. 

Compensation and Benefits

• Stable employment conditions based on an employment contract (turnover rate below 4%) 

• 1 additional vacation day for all, and 1 extra after 10 years being with us 

• Flexible working hours and possible hybrid work (presence in the office in Gdynia 3 days a week)

• English lessons during working hours 

• Internal training program to support your training needs 

• Paid employee referral program 

• Multisport Card 

• 3% employer contribution to PPK 

• Private Health Care at Medicover (extended package for employees and possibility to enroll family members) 

• Strong team-oriented and friendly work culture  

• Access to various sports activities and events 

• Modern office (well-equipped gym and playroom) close to the SKM/PKM stations