Job
Description
As a GenAI Architect, you will be responsible for designing and building highly scalable and reliable systems utilizing cutting-edge Generative AI technologies. Your expertise in system architecture, cloud infrastructure, and deep understanding of Gen AI APIs will play a crucial role in shaping the technical direction and delivering innovative solutions. Key Responsibilities: - Design and develop highly scalable, distributed, and fault-tolerant systems to handle large-scale data and requests. - Architect end-to-end solutions integrating Generative AI APIs and frameworks to meet business requirements. - Collaborate with cross-functional teams to define technical requirements. - Evaluate and select appropriate technologies, tools, and frameworks for scalability, performance, and security. - Create and maintain architectural documentation, design patterns, and best practices. - Optimize system performance, reliability, and cost efficiency for scalability during peak loads. - Stay updated on emerging Gen AI technologies, APIs, and industry trends. - Lead technical discussions, mentor engineering teams, and drive the adoption of architectural best practices. - Work closely with DevOps teams to implement CI/CD pipelines, monitoring, and incident management systems. Qualifications: Required Skills: - Proven experience in designing and implementing highly scalable, distributed systems. - Strong expertise in cloud platforms like AWS, Azure, or GCP focusing on scaling and performance optimization. - Solid understanding of Generative AI technologies, APIs, and deployment strategies. - Proficiency in programming languages such as Python, Node.js, Java, or Go. - Deep knowledge of microservices architecture, API design, and asynchronous communication patterns. - Experience with containerization (e.g., Docker) and orchestration tools (e.g., Kubernetes). - Strong understanding of data storage solutions, including SQL, NoSQL, and distributed databases. - Familiarity with security best practices in distributed systems and cloud architectures. Preferred Skills: - Experience with Machine Learning pipelines, model serving, and inference optimization. - Knowledge of AI frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers. - Hands-on experience with monitoring and observability tools like Prometheus, Grafana, or New Relic. - Exposure to event-driven architectures and message brokers like Kafka or RabbitMQ. - Background in optimizing cost and performance for high-traffic systems. Education & Experience: - Bachelors or Masters degree in Computer Science, Engineering, or related field (or equivalent experience). - 8+ years of experience in system architecture, distributed systems, and scalable application development.,
