AI/ML Engineer

About the Role: The Client is 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.