Gen AI Architect

Role: Gen AI ArchitectLocation: New Brunswick, New Jersey, USAWork Arrangement: Hybrid / OnsiteExperience: 15+ YearsAbout The OpportunityOne of our clients, an AI-native start-up, is looking for an experienced Gen AI Architect with strong hands-on expertise in Generative AI, Large Language Models (LLMs), and Cloud-Native ArchitectureThe ideal candidate will have recently designed and developed LLM-powered solutions within a product development environment, translating business requirements into scalable, secure, and production-ready AI applications.Key ResponsibilitiesDesign and architect scalable Generative AI and LLM-based solutions.Lead the development of AI-powered features and solutions within product engineering environments.Translate business requirements into effective AI/ML solutions.Evaluate, benchmark, and optimise LLMs for different business use cases.Lead technical proof of concepts (PoCs) and validate architectural feasibility.Collaborate with Data Scientists, ML Engineers, and Business Analysts to deliver AI solutions.Ensure solutions meet cloud-native architecture, performance, security, and scalability requirements.Required Skills & Experience15+ years of overall technology experience, with strong expertise in AI/ML and Generative AI.Recent hands-on experience building and deploying LLM-based solutions.Strong understanding of Transformer architectures and models such as BERT, RoBERTa, GPT, and Llama.Experience with Google Cloud Platform (GCP) and large-scale data processing.Proficiency in Python, TensorFlow/PyTorch, NumPy, Pandas, and Scikit-learn.Strong understanding of cloud-native architecture, APIs, microservices, and distributed systems.Experience with model evaluation, benchmarking, and performance optimisation.Knowledge of CI/CD, infrastructure automation, SQL/NoSQL databases, and cloud security.Experience with Python and C/C++ programming, including performance analysis and debugging.Good to HaveExperience with Vertex AI, Amazon SageMaker, Kubernetes, and Google Kubernetes Engine (GKE).Knowledge of data parallelism and model parallelism.Experience with enterprise AI strategy, data platforms, and data warehouse technologies.Exposure to GPU computing and CUDA.