AI Infrastructure Engineer – Generative AI Model Serving (Go / Rust / GPU)
Teradata (company site, opens in a new tab)3+ yrsRemoteDirect career pageEst. 18–35 LPA
Posted 13h ago
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Job description
Structured overview of role & requirements
About This Role
- Design, build, and operate scalable, low-latency model-serving platforms for LLMs and generative AI models including GPU-accelerated inference and multi-tenant isolation.
- Develop high-performance backend services in Go and Rust, optimize GPU inference throughput and latency, and manage GPU cluster infrastructure on Kubernetes across cloud and on-premises.
- Own full delivery lifecycle including CI/CD pipelines, model packaging, automated performance testing, and build AI observability tooling for monitoring model serving performance and reliability.
Minimum Requirements
- 3+ years of professional software engineering experience in backend, distributed systems, or AI/ML infrastructure.
- Strong production-level proficiency in Go and working proficiency or demonstrated passion for Rust.
- Hands-on experience with GPU computing for ML (CUDA fundamentals, GPU profiling), and practical experience serving LLMs or generative models in production with inference optimization.
- Bachelor's or Master's degree in Computer Science, Engineering, or equivalent practical experience.
Ideal Candidate Profile
- Experienced in end-to-end ownership of AI/ML infrastructure systems serving large-scale generative models, including Kubernetes-based GPU cluster management.
- Strong system design skills with deep understanding of distributed systems, concurrency, failure modes, and performance tuning, especially in Go and Rust.
- Comfortable building both backend APIs and pragmatic frontend tooling (React or similar) for AI engineering teams, with strong focus on reliability, observability, and iterative performance benchmarking.