AI/ML Developer - Google Cloud Platform

As an AI/ML Engineer in the GenAI team at our company, you will be joining our AI & Data Services practice. Your role will involve designing, developing, and deploying AI/ML solutions on Google Cloud Platform (GCP) while collaborating with cross-functional teams to deliver impactful outcomes. Key Responsibilities: - Hands-on Solution Architecture: Architect, design, develop, and deploy end-to-end, production-grade AI/ML and data analytics solutions on GCP. This includes writing code for critical modules and frameworks. - Technical Implementation: Lead the implementation of complex projects, including building scalable data pipelines, developing and optimizing ML models, and ensuring the technical integrity of the final solution. - Client Technical Advisory: Serve as the primary technical expert during client engagements. Directly interface with client-side architects and engineers to design and troubleshoot complex systems for national and international clients. - Prototyping & Innovation: Lead proof-of-concept (PoC) development and rapid prototyping to demonstrate the feasibility of new AI solutions and technologies for enterprise-scale problems. - Performance Optimization: Identify and resolve performance bottlenecks in data processing, model training, and inference to ensure solutions are highly optimized and efficient. Qualifications Required: - 3+ years of demonstrable hands-on experience designing, building, and deploying mission-critical AI, Machine Learning, and Data Analytics systems. - Expert-level proficiency in Python and its core data science libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy). - Deep, hands-on expertise with the Google Cloud Platform (GCP) with proven experience using services like Vertex AI, BigQuery, Google Kubernetes Engine (GKE), Dataflow, and Pub/Sub in production environments. - Must have a background in the technology services or consulting industry with a portfolio of successfully delivered projects. - Significant hands-on project experience within the Banking, Financial Services, and Insurance (BFSI). - Proven ability to lead technical delivery for large-scale enterprise projects while remaining deeply involved in the implementation details. - Strong practical experience with Agile methodologies, CI/CD pipelines, and MLOps principles. Good-To-Have (Preferred): - Hands-on experience with the NVIDIA technology stack (e.g., CUDA, cuDNN, Triton Inference Server) is a significant plus. - Experience with containerization and orchestration technologies like Docker and Kubernetes. - Contributions to open-source projects in the AI/ML space.,