Description :

Experience : 11 - 15 Years

Location : Hyderabad

Role Focus : System design, distributed infrastructure, and AI strategy.

Role Overview :

We are seeking a visionary AI Architect to define the structural foundation of our enterprise AI ecosystem. As a "System Architect," you will be responsible for the high-level design of scalable, cost-effective, and maintainable ML systems.

Your focus will be on distributed training pipelines, enterprise-grade model serving strategies, and the long-term scalability of our AI infrastructure.

You will collaborate with global business leaders to ensure our AI initiatives align with broader technological trends and responsible AI practices.

Key Responsibilities :

- System Design & Governance : Architect end-to-end ML systems that are scalable and secure, defining the "North Star" for our model serving and storage strategies.

- Distributed Training : Design large-scale distributed training pipelines using Ray, Dask, or Spark and implement efficient model compression techniques.

- Enterprise Infrastructure : Lead architectural decisions for real-time and batch inference systems, including the integration of Feature Stores and Model Registries.

- Inference Optimization : Architect efficient inference strategies using vLLM, TensorRT-LLM, and advanced quantization methods for resource-constrained environments.

- Strategic Roadmap : Work with product and business teams to translate long-term goals into technical AI solutions and infrastructure roadmaps.

- Responsible AI : Establish frameworks for model governance, compliance, and ethical AI practices across the organization.

- Cloud Architecture : Design multi-cloud or hybrid-cloud ML services utilizing AWS, Azure, or GCP at an enterprise scale.

Technical Requirements

- Architectural Depth : 6+ years specifically designing and implementing production-scale ML systems.

- Infrastructure Mastery : Deep understanding of model serving architectures (gRPC, REST, Batch) and Service Mesh integration.

- Big Data & Streaming : Expert knowledge of Kafka, Spark, and Airflow for large-scale data orchestration.

- Advanced NLP : Proven experience with Knowledge Graphs and complex deep learning architectures.

Recommended Resources for the Team

To help your new AI leadership team stay at the forefront of GenAI and System Design, these resources are highly recommended :

- Generative AI with LangChain : A deep-dive for the AI Lead on building production-ready LLM applications.

- Designing Data-Intensive Applications : The "gold standard" for the AI Architect to ensure system reliability and scalability.

- Machine Learning Engineering on AWS : A practical guide for implementing MLOps at an enterprise scale