We’re seeking a Machine Learning Engineer to design, train, and deploy scalable ML models that power data-driven products. You’ll collaborate with data scientists and backend teams to bring cutting‑edge research into production — combining traditional ML with next‑gen GenAI and LLM‑based systems . Key Responsibilities Build and maintain end-to-end ML pipelines — from data preprocessing to deployment. Design, implement, and evaluate personalization , recommendation , and candidate‑selection algorithms . Develop and apply Graph Neural Networks (GNNs) and hybrid systems combining ML with LLMs. Optimize performance, accuracy, and model latency. Work with big data technologies (Spark, Airflow, Databricks) to ensure scalability. Collaborate with DevOps and engineering teams for MLOps integration . Lead and mentor team members, fostering innovation and technical excellence. Requirements Strong programming skills in Python and experience with ML frameworks (TensorFlow, Scikit-learn, PyTorch). Proven background in statistics , data modeling , and feature engineering . Experience deploying models via APIs or cloud platforms (AWS Sagemaker, Vertex AI, Azure ML) . Solid understanding of CI/CD , version control (Git), and containerization (Docker, Kubernetes) . Excellent communication and leadership skills to guide both technical and collaborative efforts. Nice to Have Experience in deep learning , LLMs , or reinforcement learning . Knowledge of GenAI systems , prompt engineering , and multi‑modal applications . Familiarity with data engineering tools and distributed systems. Be part of a team that transforms data into intelligent, real‑world solutions by blending the power of ML and AI innovation. #J-18808-Ljbffr