Our Company

At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.

Senior AI Platform Engineer

Teradata | AI Platform & Agentic Systems | Reports to: Sr. Manager, Software Engineering

Power the Platform Behind Enterprise Generative AI

Teradata is building the foundation for enterprise AI applications spanning Unstructured data processing, Enterprise Vector Store, Retrieval-Augmented Generation (RAG), AI agents and model integration.

As a Senior AI Platform Engineer, you will build production-grade AI systems that bridge software engineering, AI engineering and infrastructure.

This is a hands-on engineering role. You will work across application code, APIs, AI agents, vector stores, model integrations, deployment automation and infrastructure troubleshooting. You should be comfortable coding in Python / Go / Java / Rust, Kubernetes deployments, working with different Cloud providers such as AWS, Azure, GCP and OnPremise Infra, REST/gRPC APIs, agent workflows and AI model evaluation.

You will help build systems that are scalable, observable, secure and reliable across cloud, on-premises and hybrid environments.

What You’ll Do

- Core Systems & Services: Build high-performance control/data plane services and runtime components (gateways, schedulers, tokenizers) in Go, Python, Java and Rust.
- Agentic Workflows & MCP: Design production multi-agent systems and MCP servers/tools using frameworks like LangGraph, CrewAI, Autogen, etc.
- Enterprise RAG & Vector Infrastructure: Architect scalable retrieval pipelines and vector-store systems covering embedding generation, indexing, hybrid search, and reranking.
- Deployment & Platform Operations: Own infrastructure-as-code, CI/CD rollouts, and container orchestration across Docker, Kubernetes, and hybrid/cloud environments.
- Observability & Model Evaluation:



Instrument end-to-end telemetry (OpenTelemetry, Prometheus, Grafana) and automated evaluation harnesses (e.g., Langfuse, LangSmith) to benchmark latency, token throughput, GPU metrics, and hallucination/drift signals.
- Automate operations: Develop Python, Go and shell-based tooling and scripts for deployment, environment management, debugging, validation and operational automation.
- Build reliable APIs: Design and maintain well-defined REST/gRPC APIs, SDKs and service interfaces used by AI applications, Developers and platform components.
- Own production issues: Debug problems end to end across application code, APIs, Kubernetes, networking, infrastructure, models and data layers.

Who You’ll Work With You will work closely with AI/ML engineers, platform engineers, infrastructure and SRE teams, data scientists and product teams to deliver enterprise AI capabilities across Unstructured, Enterprise Vector Store and agentic AI systems.

The role requires strong end-to-end ownership. Engineers are expected to investigate problems across multiple layers of the stack rather than limiting themselves to application development.

What Makes You a Qualified Candidate

- 4+ years of professional software engineering experience in backend systems, distributed systems, AI/ML infrastructure and Agentic / GenAI.
- Robust, production-level proficiency in Python (FastMCP, FastAPI), Go (concurrency, gRPC, profiling, service design) and working proficiency, or a demonstrated passion for learning, Rust (ownership, async runtimes such as Tokio, FFI, performance tuning), with willingness to learn / adapt to different programming languages or frameworks. Experience with Java is a plus.
- Strong system design skills and a deep understanding of distributed systems, networking, concurrency, and failure modes.
- Experience with containers and orchestration (Docker, Kubernetes,



Helm) and CI/CD (GitHub Actions, Jenkins, or similar), plus infrastructure as code (Terraform, Ansible).
- Full-stack engineering experience: comfortable building REST/gRPC APIs and a pragmatic front end (React or similar) for dashboards, playgrounds, or admin tools; Python for glue, tooling, and ML integration.
- AI Observability & Evaluation: Hands-on experience instrumenting distributed AI workloads using OpenTelemetry, Prometheus, and Grafana, along with LLM evaluation platforms (LangSmith, Langfuse, or Arize Phoenix).
- You are obsessed about reliability, debuggability, and ensuring AI systems behave predictably and deterministically when needed.
- Comfortable with Linux and shell scripting and debugging production systems.
- You code daily, own systems end to end and are knowledgeable about open-source tools and know how to leverage and extend them.
- Agentic AI & MCP Systems: Production experience building agent workflows and multi-agent systems (LangGraph, CrewAI, AutoGen) leveraging Model Context Protocol (MCP), dynamic tool routing, context management, and model serving integration.
- Enterprise RAG & Data Systems: Strong architecture knowledge of enterprise RAG pipelines (chunking, hybrid retrieval, embeddings, reranking) and hands-on integration with vector databases (Pinecone, Qdrant) alongside relational/NoSQL stores (Postgres).

What You Bring

- A Bachelor’s or Master’s degree in Computer Science Engineering
- Genuine excitement for AI, LLMs, and the systems that make them efficient and affordable to run at scale.
- Strong problem-solving and debugging capability across software and infrastructure.
- Strong communication skills and a bias toward measuring, benchmarking, and iterating.

Bonus Points

- Familiarity with GPU infrastructure and AI inference optimization.
- Experience with security and compliance requirements for enterprise AI.
- Experience deploying software into on-premises, restricted or air-gapped environments.
- Contributions to open-source AI, infrastructure or distributed systems projects.
- Familiarity with modern data platforms such as Teradata, Snowflake, or Databricks is a plus.

📌 Senior AI Platform Engineer (Bengaluru)
🏢 Teradata
📍 Bengaluru