01Key Responsibilities
- Design and implement end-to-end architectures for agentic AI systems, Generative AI platforms, RAG pipelines, and AI inference workflows. Build scalable AI platforms supporting distributed training, model deployment, and enterprise integration. Optimise system performance, latency, throughput, GPU utilisation, and infrastructure costs. Lead debugging, production incident resolution, performance tuning, and reliability improvements. Develop reusable AI frameworks, SDKs, APIs, connectors, and CI/CD templates. Implement observability, monitoring, logging, tracing, and operational dashboards. Establish engineering standards, architecture governance, testing frameworks, and controlled release processes. Collaborate with research, product, data, and platform teams to translate AI research into production-ready systems. Mentor engineering teams and drive best practices in software engineering, MLOps, and AI platform development. Required Skills:- Strong expertise in AI system architecture, distributed systems, cloud platforms, and scalable backend engineering. Experience with Generative AI, LLM deployment, RAG systems, agentic AI frameworks, and AI orchestration platforms. Proficiency in Python and modern software engineering practices. Experience with MLOps/LLMOps, containerisation, Kubernetes, CI/CD, monitoring, and infrastructure automation. Knowledge of distributed training, GPU optimisation, vector databases, and real-time inference systems. Strong debugging, performance optimisation, and reliability engineering skills. Excellent technical leadership, collaboration, and mentoring abilities. Qualifications & Experience:- Bachelor s, Master s, or PhD in Computer Science, Artificial Intelligence, Software Engineering, or a related field. 10 15 years of experience in software engineering, AI platforms, machine learning infrastructure, or distributed systems. Proven experience designing and delivering large-scale production systems and AI platforms. Experience leading technical architecture, engineering standards, and cross-functional delivery initiatives. Preferred Attributes:- Hands-on principal-level engineering expertise. Strong understanding of security, governance, observability, and production operations. Experience building reusable engineering platforms and developer tooling. Ability to translate advanced AI research into reliable enterprise-grade products. .
