Senior Machine Learning Engineer (Time Series, AWS, Industrial AI)

Essential functions

• Contribute to the design, development, and deployment of end-to-end ML pipelines from scratch, with a strong focus on time series data (e.g., sensor readings, telemetry).

• Collaborate closely with DevOps and Cloud teams to define system architecture and ensure smooth integration of ML components into a scalable cloud infrastructure (AWS preferred).

• Select and train appropriate ML models (forecasting, anomaly detection, recommendation systems), monitor their performance, and implement retraining workflows as needed.

• Ensure high performance, scalability, and reliability of deployed models in a production environment.

• Participate in technical discussions, code reviews, and strategic architectural decisions.

• Drive continuous improvement in ML workflows through automation (CI/CD), observability, and MLOps best practices.

• Translate complex requirements into technical solutions and maintain clear documentation and communication with both technical and non-technical stakeholders.

• Stay up to date with emerging trends in machine learning, cloud technologies, and industrial data science applications.

Qualifications

• 5+ years of experience as a Machine Learning Engineer, with at least 2 year focused on time series or predictive modeling in real-world production systems.

• Strong background in mathematics or statistics – a degree in Mathematics, Physics, or related fields is a strong plus.

• Hands-on experience with AWS services for ML, including Amazon SageMaker, Kinesis, Lake Formation, S3, Lambda.

• Proficiency in Python and core ML/data libraries (e.g., pandas, scikit-learn, NumPy, TensorFlow or PyTorch).

• Experience with Docker, container orchestration (Kubernetes, AWS EKS), and working in cloud-native environments.

• Understanding of CI/CD for ML, including tools like GitLab CI, Jenkins, or Azure DevOps.

• Familiarity with version control and reproducibility tools (e.g., MLflow, DVC).

• Good knowledge of SQL and experience with databases such as PostgreSQL, SQLServer, and time-series solutions like TimescaleDB.

Would be a plus

• Prior experience working with industrial or manufacturing data, such as gas turbines, compressors, or sensor-based systems.

• Exposure to Infrastructure as Code (IaC) tools (e.g., AWS CloudFormation, Terraform).

• Familiarity with GenAI systems, recommendation engines, or Large Language Models.

• Knowledge of monitoring tools (e.g., AWS CloudWatch) and ML model explainability frameworks (e.g., SHAP, LIME).

We offer

• Work on cutting-edge AI/ML projects in a cross-functional team of experienced engineers and data scientists.

• Flexible working hours and a remote-friendly setup.

• Access to specialisation courses and learning resources.

• 24 days annual leave + 5 additional sick days.

• Floating holidays.

• Private medical subscription for employees and their family members.

• A benefits basket worth €650/year to spend on wellness, education, tech, and more.

About us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.