Note: The job is a remote job and is open to candidates in USA. Teradata is hiring a Staff AI Engineer to design, evolve, and scale its enterprise AI platforms and capabilities. The role leads architecture and technical strategy across agentic AI, LLM platforms, RAG, vector stores, and developer-facing AI systems, while mentoring engineers and driving reliability and operational excellence.
Responsibilities
- Lead the design and evolution of largescale, distributed AI systems that power Teradata’s AI platform and AI native products
- Own end-to-end architecture for critical AI capabilities such as agentic workflows, RAG pipelines, vector search, semantic retrieval, and AI orchestration frameworks
- Drive technical strategy and architectural consistency across multiple engineering teams
- Design and implement production grade AI systems using LLMs, embeddings, vector databases, and agent based architectures
- Build scalable, secure, and reusable platform services and APIs supporting AI workloads across the software development lifecycle
- Define and implement guardrails for reliability, safety, governance, and cost control in enterprise AI systems
- Partner with product management, architecture, research, and cloud platform teams to translate business requirements into scalable AI solutions
- Influence roadmap decisions by providing deep technical insight, tradeoff analysis, and long-term platform thinking
- Act as a technical escalation point for complex system design, performance, and reliability challenges
- Drive best practices for testing, observability, evaluation, and production readiness of AI systems
- Identify systemic performance bottlenecks and lead efforts to optimize distributed systems and AI pipelines
- Establish engineering standards that improve development velocity, quality, and operational resilience
- Mentor Senior and Staff level engineers, providing guidance on architecture, design reviews, and technical decision making
- Raise the overall engineering bar through design forums, technical reviews, and knowledge sharing
- Lead by example through hands on contributions to the most complex and business critical problems
Skills
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
- 8+ years of experience building backend services, distributed systems, or data/AI platforms
- Strong proficiency in Java, Go, or Python, with experience building largescale services
- Deep understanding of distributed system design, scalability, fault tolerance, and cloud native architectures
- Proven experience designing and operating production systems with SQL and NoSQL data stores
- Experience with LLMs, embeddings, vector databases, and AI orchestration frameworks
- Exposure to agentic AI patterns such as tool calling, planning, memory, and multi-step reasoning
- Experience building or operating AI systems in cloud environments (AWS, Azure, or GCP)
- Familiarity with Kubernetes, Docker, CI/CD pipelines, and production-grade observability
Benefits
- Flexible work model, with flexibility about how, when, and where you work
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