Description

You will design, build, and productionize ML-driven observability solutions on large-scale session data including RUM, logs, WebRTC, and clickstreams.

This role is remote.

Responsibilities

• Develop supervised and unsupervised learning models, including time-series forecasting (Prophet, ARIMA, Deep Learning) and NLP (Transformers, BERT).
• Build GenAI applications leveraging LLMs, RAG, and Agentic AI architectures.
• Implement multimodal ML systems and advanced clustering/classification techniques (Hierarchical, KMeans, DBSCAN, XGBoost).
• Engineer features using TF-IDF and embeddings, and deploy models via Airflow, Kubeflow, Docker, and Kubernetes.
• Manage high-scale event data pipelines using PySpark, Spark, and Kafka, ensuring robust API and microservices integration.

Required Skills

• 5+ years of experience in Machine Learning Engineering or Data Science.
• Strong proficiency in Python for data processing and model development.
• Hands-on experience with NLP, GenAI, LLMs, RAG, and Agentic AI frameworks.
• Expertise in PySpark, Spark, and Kafka for streaming and large-scale data pipelines.
• Deep understanding of microservices architecture and API design.
• Practical knowledge of MLOps tools including Airflow, Kubeflow, Docker, and Kubernetes.
• Experience with time-series modeling, clustering, and tree-based classification models.

Preferred Skills

• Experience with WebRTC and RUM (Real User Monitoring) data analysis.
• Background in multimodal machine learning systems.

Key Skills

Education

Any Graduate