Overview

We are looking for an Artificial Intelligence Engineer (II) to design, build, evaluate, and improve AI capabilities for enterprise data and database systems.

Responsibilities

• We are looking for an Artificial Intelligence Engineer (II) to design, build, evaluate, and improve AI capabilities for enterprise data and database systems. You will work across AI/ML, vector search, Retrieval-Augmented Generation (RAG), AI agents, and production software engineering, helping build scalable AI capabilities for Teradata’s enterprise customers.
• Design and implement AI/ML capabilities and intelligent applications for enterprise data and database workloads.
• Develop and enhance vector search and retrieval capabilities, including algorithms and techniques such as HNSW, IVF, Flat search, Product Quantization (PQ), and other vector indexing and quantization approaches.
• Develop Retrieval-Augmented Generation (RAG) and Agentic RAG solutions that combine enterprise data, vector search, large language models (LLMs), APIs, tools, and contextual information.
• Build components for AI agents and agentic workflows, including tool invocation, structured outputs, context management, MCP-based integrations, and multi-step workflows.
• Develop AI capabilities using Python, C++, Rust, or similar programming languages, with a focus on performance, scalability, maintainability, and platform independence.
• Build software that can operate across AWS, Azure, GCP, on-premises, and hybrid environments, minimizing dependencies on any specific cloud platform.
• Apply software engineering best practices including unit testing, integration testing, code reviews, observability, security, documentation, and performance optimization.
• Work with senior engineers and architects to translate product requirements and customer use cases into technical designs, implementation plans, and production-quality software.
• Contribute to technical investigations, prototypes, proof-of-concepts, and new AI capabilities as the technology and product landscape evolves.
• Continuously learn and apply emerging developments in Generative AI, LLMs, vector databases, agentic AI, information retrieval, model optimization, and AI engineering.
• AI Architects and Senior AI Engineers to design scalable AI architectures and solve complex technical problems.
• ML/AI Engineers and Software Engineers to implement vector search, RAG, agentic AI, and database-integrated AI capabilities.
• Database and Platform Engineering teams to integrate AI functionality with Teradata database technologies and core platform services.
• Product Managers and UX teams to understand customer requirements and translate use cases into practical AI capabilities.
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Computer Engineering, or a related technical field.
• 2+ years of professional software engineering, AI/ML, or data engineering experience, with hands-on development experience.
• Strong programming experience in at least one of C++, Python, Rust, Java, or a similar programming language.
• Experience building or integrating AI/ML, NLP, Generative AI, or LLM-based applications.
• Understanding of machine learning concepts, embeddings, information retrieval, vector search, and similarity search.
• Experience working with SQL, relational databases, structured data, or database-integrated applications.
• Experience developing software using APIs, microservices, SDKs, or distributed systems concepts.
• Understanding of software engineering fundamentals including data structures, algorithms, object-oriented/design principles, testing, debugging, and version control.
• Ability to analyze technical problems, investigate failures, and develop robust and maintainable solutions.
• Strong written and verbal communication skills, with the ability to collaborate effectively across engineering and cross-functional teams.

Qualifications

• Hands-on experience with Generative AI, LLMs, RAG, Agentic AI, or AI application development.
• Familiarity with vector databases, vector indexes, embeddings, similarity search, or approximate nearest-neighbor (ANN) algorithms.
• Exposure to technologies such as HNSW, IVF, Product Quantization, or other vector search/optimization techniques.
• Experience with frameworks or technologies such as LangChain, LangGraph, MCP, agent frameworks, embedding models, vector databases, or similar AI frameworks.
• Experience working with structured and unstructured data ingestion and retrieval pipelines.
• Experience developing applications that run across multiple cloud platforms or cloud-agnostic environments is a plus.
• Understanding of AI observability, evaluation, testing, reliability, security, and responsible AI practices.
• Experience with Docker, Kubernetes, CI/CD, Linux, REST APIs, or distributed systems is a plus.
• Strong problem-solving mindset with the ability to work through ambiguous technical problems and learn new technologies quickly.
• Ability to work effectively in a collaborative engineering environment and take ownership of assigned technical deliverables from design through implementation and validation.
• A strong interest in keeping current with rapidly evolving AI/ML and Generative AI technologies and applying them to real-world enterprise problems.