About the Role 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 recommender systems, natural language processing, or graph-based search engines. • Contributions to open-source ML libraries or published research in AI/ML.