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Get AI-powered advice on this job and more exclusive features. Direct message the job poster from Ubique Systems We are seeking an experienced AI/ML Engineer to help enterprise clients accelerate their adoption of advanced machine learning technologies. This role will focus on building graph-based neural network (GNN) models, generating ScaNN-based embeddings, and training scalable ML models for search, recommendation, and classification systems. You will collaborate closely with Google Cloud engineers, architects, and data scientists to deliver innovative, production-ready AI solutions. Key Responsibilities Design and implement Graph Neural Network (GNN) architectures for enterprise-scale applications. Develop and optimize vector embedding generation pipelines using ScaNN or similar ANN techniques. Train, fine-tune, and deploy ML/DL models using TensorFlow, PyTorch, JAX, or similar frameworks. Collaborate with data engineers and solution architects to integrate models into scalable cloud solutions. Perform model evaluations, A/B testing, and hyperparameter tuning for optimal performance. Build reusable pipelines and tools for ML training, deployment, and monitoring on GCP. Engage directly with customer technical teams to understand business needs and translate them into ML solutions. Produce technical documentation and presentations for internal and customer-facing stakeholders. Required Qualifications Bachelor’s degree in computer science, Mathematics or a related technical field or equivalent practical experience. Certifications Minimum: Google Professional Data Engineer Preferred: AWS Machine Learning Specialty Certification 7+ years in a customer facing role working with enterprise clients 4+ years of experience working in enterprise data warehouse and analytics technologies Hands-on experience building and training machine learning models. Experience writing software in one or more languages such as Python, Scala, R, or similar with strong competencies in data structures, algorithms, and software design. Experience working with recommendation engines, data pipelines, or distributed machine learning. Experience working with deep learning frameworks (such as TensorFlow, Keras, Torch, Caffe, Theano). Strong coding skills in Python and familiarity with ML/DL libraries like TensorFlow, PyTorch, or JAX. Knowledge of data analytics concepts, including data warehouse technical architectures, ETL and reporting/analytic tools and environments (such as Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce, Flume). Customer facing experience of discovery, assessment, execution, and operations. Demonstrated excellent communication, presentation, and problem solving skills. Experience in project governance and enterprise customer management. Proficiency in building Graph Neural Networks (GNNs) using frameworks like DGL, PyTorch Geometric, or similar. Experience with ScaNN or other approximate nearest neighbor (ANN) techniques for vector similarity search. Hands-on experience with Google Cloud Platform (GCP) tools such as Vertex AI, BigQuery, and Dataflow. Strong problem-solving and communication skills, including the ability to work with clients and cross-functional teams. Preferred Qualifications PhD in Computer Science, AI/ML, or related field. Experience with production ML systems and MLOps pipelines using Kubeflow or Vertex AI Pipelines. Knowledge of transformers and large language models (LLMs). Understanding of recommended systems, natural language processing, or graph-based search engines. Contributions to open-source ML libraries or published research in AI/ML. 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