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.
Who You’ll Work With
You will work as part of a collaborative Artificial Intelligence Engineering team building foundational AI capabilities for Teradata’s enterprise data platform.
You’ll work closely with
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.
You will work in a highly collaborative environment where engineers are expected to learn quickly, take ownership, contribute technically, and work across multiple areas of AI engineering.
What Makes You a Qualified Candidate / Minimum Requirements
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.
What You’ll Bring
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.
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