Software AI 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:

Contribute to the design and development of software components that enable autonomous AI agents to reason, plan, act, and learn.

Implement features that support agentic capabilities such as tool use, memory, long-term planning, task execution, and self-reflection.

Integrate with state-of-the-art LLMs (e.g., OpenAI, Anthropic) and agent orchestration frameworks (e.g., LangChain, CrewAI, AutoGen, Flowise).

Design and implement integrations with the Model Context Protocol (MCP) to facilitate standardized context sharing and management among AI agents and tools.

Develop and maintain interfaces with major Cloud Service Providers (CSPs) — AWS, Azure, GCP — to enable scalable deployment, resource provisioning, and cloud-native orchestration of autonomous agent workflows.

Write, test, and debug Python or other backend code that powers agent workflows in cloud-native environments.

Assist in building interfaces between agents and tools such as APIs, databases, search engines, and user-facing applications.

Participate in Agile development workflows — sprint planning, code reviews, retrospectives — and collaborate across AI, product, and engineering teams.

Work with structured and unstructured data to support context-aware reasoning and decision-making in agents.

Contribute to the development of internal testing tools and evaluation methods for autonomous agent behaviors.

Support the deployment of agent systems in scalable environments using Kubernetes, Docker, and cloud services (AWS, Azure, or GCP).

Maintain documentation of agent capabilities, system design, and integration strategies.

What makes you a qualified candidate:

1–3 years of experience (or strong academic/internship background) in software development with a focus on AI/ML, intelligent systems, or cloud-native applications.

Strong interest in Agentic AI — systems where AI agents can autonomously interpret goals, take actions, and learn from outcomes.

Familiarity with one or more programming languages: Python (preferred), Go, Java, or C++.

Exposure to LLMs and AI APIs (e.g., OpenAI, Hugging Face, Claude) and understanding of prompting, embeddings, or vector databases.

Basic understanding of how AI agents use tools and memory to solve complex tasks.

Experience with RESTful APIs, backend service design, and microservice architectures.

Experience with Model Context Protocol (MCP) implementation or integration for managing agent context and state.

Familiarity with modern cloud infrastructure (AWS, GCP, or Azure), especially in deploying containerized applications.

Ability to write clean, testable code and follow best practices in code reviews and version control.

Clear communicator, with a willingness to ask questions, propose ideas, and collaborate across teams.

Passion for learning emerging AI techniques and contributing to real-world agentic applications.

What you will bring:

BS degree in Computer Science, Artificial Intelligence, Software Engineering, or related technical field.

Strong problem-solving skills and a solid foundation in data structures, algorithms, and object-oriented programming.

Experience with cloud tools and platforms (AWS, GCP, Azure), especially Kubernetes and Docker.

Exposure to agentic frameworks such as LangChain, AutoGen, CrewAI, or similar tools.

Hands-on experience or familiarity with Model Context Protocol (MCP) for agent context management.

Hands-on experience or working knowledge of cloud-native deployment and orchestration on CSP platforms.

Curiosity and excitement about building intelligent systems that act autonomously and adaptively.

A mindset geared toward experimentation, continuous learning, and pushing the boundaries of AI capabilities.