What You’ll Do 

As a Senior AI Engineer, you will be a key contributor to Teradata’s AI platform and product initiatives. You will design, build, and scale high‑quality AI-powered systems—ranging from agentic AI solutions and AI marketplaces to core platform capabilities such as RAG, vector search, and developer-facing AI tools. You will work in a technically deep, fast-paced environment, collaborating closely with platform, product, and engineering teams to shape Teradata’s next-generation AI capabilities.

Key Responsibilities:

• Design, develop, and maintain scalable, reliable, and high-performance services and applications for AI-driven platforms.
• Apply and experiment with AI/ML technologies, including large language models (LLMs), agentic AI, and retrieval-augmented generation (RAG) pipelines.
• Build and integrate robust RESTful APIs with a strong focus on security, data consistency, and maintainability.
• Design and implement components such as vector stores, semantic search, AI agents, orchestration workflows, and evaluation frameworks where applicable.  
• Collaborate with cross-functional teams (AI/ML engineers, product managers, architects, and cloud engineers) to deliver end-to-end features aligned with product roadmaps.  
• Mentor and guide engineers through technical planning, design reviews, implementation, and best practices.  
• Identify, diagnose, and resolve system performance and reliability issues across distributed systems.  
• Write unit and integration tests, participate in code reviews, and uphold engineering excellence.  
• Stay current with emerging trends in AI/ML, cloud-native architectures, and enterprise-scale AI systems.  

What Makes You a Qualified Candidate 

• Bachelor’s degree in Computer Science, Engineering, or an equivalent field from a recognized institution.  
• 4+ years of experience in backend services, distributed systems, or data platform development.  
• Strong proficiency in Java, Go, or Python for service and platform development.  
• Solid understanding of distributed system design principles, scalability, and cloud-native architectures.  
• Hands-on experience working with SQL and NoSQL data stores and designing efficient data access patterns.  

What You’ll Bring 

• 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.

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