Responsibility
Job Title: Machine Learning Engineer / Data Scientist
Experience: 8+ Years
Employment Type: Contract
Location: Remote
Role Summary
We are looking for an experienced Machine Learning Engineer / Data Scientist to design, develop, and productionize ML-driven observability solutions using large-scale session and event data, including Real User Monitoring (RUM), logs, WebRTC, and clickstream data.
The ideal candidate will have strong expertise in behavioral inference, anomaly detection, time-series modeling, multimodal machine learning, and scalable ML systems. The role focuses on improving user experience insights while helping reduce Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR).
Key Responsibilities
- Design and develop scalable machine learning solutions for behavioral analysis, anomaly detection, and user experience monitoring.
- Build and optimize ML models using large-scale session, log, clickstream, and WebRTC data.
- Develop multimodal ML systems by combining multiple data signals to identify patterns, anomalies, and performance issues.
- Build scalable data processing and ML pipelines using Python, PySpark, Spark, and Kafka.
- Develop and deploy ML services using APIs, microservices, Docker, Kubernetes, and MLOps frameworks.
- Implement model versioning, monitoring, evaluation, and continuous improvement processes.
- Collaborate with engineering, data, and observability teams to translate business and operational requirements into production-ready ML solutions.
Required Skills & Experience
- 8+ years of experience in Data Science, Machine Learning Engineering, or related roles.
- Strong foundation in:
- Supervised and Unsupervised Learning
- Anomaly Detection
- Time-Series Modeling using Prophet, ARIMA, and Deep Learning
- Natural Language Processing (NLP)
- Transformers and BERT-based models
- Generative AI, including LLMs, RAG, and Agentic AI
- Multimodal Machine Learning
- Strong hands-on experience with Python and distributed data processing using PySpark / Apache Spark.
- Experience working with high-volume event data and streaming platforms such as Apache Kafka.
- Strong understanding of APIs, microservices, and scalable data pipelines.
- Experience with:
- Clustering techniques, including Hierarchical Clustering, K-Means, and Density-Based Clustering
- Classification models, including XGBoost and Tree-Based Models
- Feature extraction using TF-IDF and Embeddings
MLOps & Deployment
- Experience building and managing ML pipelines using Kubeflow or similar MLOps platforms.
- Hands-on experience with Docker and Kubernetes.
- Knowledge of model deployment, versioning, monitoring, and performance evaluation.
- Experience developing scalable and production-ready ML systems.
Good-to-Have Skills
- Experience with Observability or Real User Monitoring (RUM) tools.
- Knowledge of WebRTC, audio signal processing, or real-time communication systems.
- Exposure to LLMs, RAG, prompt engineering, and agentic workflows.
- Understanding of frontend performance metrics, including:
- LCP (Largest Contentful Paint)
- INP (Interaction to Next Paint)
- CLS (Cumulative Layout Shift)