Staff Engineer

Our Company:

At Teradata, we believe that people thrive when empowered with better information. That's why we built the most complete cloud analytics and data platform for AI. By delivering harmonized data, trusted AI, and faster innovation, we uplift and empower our customers—and our customers' customers—to make better, more confident decisions. The world's top companies across every major industry trust Teradata to improve business performance, enrich customer experiences, and fully integrate data across the enterprise.

What you will do:

In this role you will lead a critical and highly visible function within Teradata Vantage platform. You will be given the opportunity to autonomously deliver the technical direction of the service, and the feature roadmap. You will work with extraordinary talent and have the opportunity to shape the team to best execute on the product.

Job Responsibilities

Lead the design and development of complex, distributed software systems that power Teradata's next-generation data and analytics platforms.

Architect high-scale, cloud-native backends ensuring reliability, scalability, observability, and performance across global environments.

Drive end-to-end technical delivery for major product initiatives — from requirements and architecture through implementation, testing, and deployment.

Mentor and guide engineers, providing technical leadership, code reviews, and architectural direction.

Partner with product managers, UX designers, and cross-functional engineering teams to translate product vision into robust, performant software solutions.

Define and enforce engineering best practices for code quality, testing, observability, and operational excellence.

Develop and optimize APIs, microservices, and distributed systems that handle large-scale workloads efficiently.

Collaborate with DevOps and ML/AI teams to build reliable infrastructure for data-intensive and AI-enabled applications , including support for model training, inference, and integration.

Design and implement data pipelines and services that enable downstream AI and analytics workflows.

Work with emerging technologies and frameworks to enable AI model serving , monitoring, and lifecycle management within production systems.

Identify performance bottlenecks, propose improvements, and drive systemic enhancements across services and infrastructure.

Stay ahead of technology trends, particularly in cloud infrastructure, distributed systems, and applied AI engineering.

What Makes You a Qualified Candidate

8+ years of experience in software engineering, with a proven record of delivering large-scale, production-grade systems.

Expertise in backend and distributed system architecture , including concurrency, performance optimization, and fault tolerance.

Strong development experience in Python, Go, Java, or similar programming languages.

Proficiency with cloud platforms (AWS, Azure, GCP) and container orchestration (Docker, Kubernetes).

Experience architecting microservices and event-driven systems.

Solid understanding of DevOps/MLOps practices — including CI/CD pipelines, infrastructure-as-code (Terraform, Ansible), and observability tooling.

Exposure to AI/ML system integration , such as connecting backend systems to model-serving frameworks or AI APIs (e.g., OpenAI, Hugging Face, Triton Inference Server, ONNX Runtime).

Experience working with data pipelines , streaming systems , or vector databases (e.g., Kafka, Flink, Pinecone, FAISS) that support AI-driven features.

Familiarity with AI concepts such as model inference, retrieval-augmented generation (RAG), or embedding-based search — with the ability to design scalable systems that enable these capabilities.

Excellent communication skills with the ability to convey complex technical concepts across teams.

Proven leadership in mentoring engineers, leading design reviews, and driving architectural consistency across systems.

What You Will Bring

BS or MS degree in Computer Science, Software Engineering, or a related technical field.

Strong foundation in software architecture , system design, and distributed computing principles.

Ability to collaborate across engineering, AI, and product teams to deliver scalable, maintainable, and intelligent systems.

A pragmatic and forward-looking mindset — balancing technical depth with delivery speed and business value.

Passion for innovation and continuous improvement , with curiosity to explore how AI can enhance modern software systems.