Agentic AI Platform (Full Stack)

As a Senior Data Engineer, you will be responsible for designing and building core components of the Agentic AI platform, developing scalable data pipelines, contributing to platform abstractions, integrating various data sources, and participating in architecture and code reviews. You will own features across the full SDLC and collaborate with other teams to enhance ACPs AI and data PaaS offerings. Your role will involve following open-source-first and cloud-native engineering practices. Key Responsibilities: - Design and build core components of the Agentic AI platform such as agent workflows, memory, and execution runtime - Develop and operate scalable data pipelines supporting LLMs and AI agents - Contribute to building platform abstractions for secure agent execution - Integrate LLMs, vector stores, and enterprise data sources into reliable systems - Participate in architecture, design, and code reviews for platform services - Own features across the full SDLC including design, development, testing, deployment, and production support - Collaborate with infra, platform, and AI teams to evolve ACPs AI and data PaaS offerings - Follow open-source-first and cloud-native engineering practices Qualifications Required: - 68 years of experience in backend or data engineering - Strong hands-on experience building scalable distributed systems - Proficiency in Java (preferred) or other backend languages like Python, Scala, or Go - Solid fundamentals in data engineering (batch & streaming pipelines), distributed systems, microservices, APIs, messaging, and event-driven architectures - Practical exposure to LLMs and AI-driven systems - Experience across end-to-end SDLC, including CI/CD and production operations - Strong problem-solving and system design skills Additional Details: - Exposure to Agentic AI concepts or frameworks like LangChain, LangGraph, LlamaIndex, AutoGen, etc. is a plus - Experience with public cloud platforms such as AWS, GCP, Azure - Knowledge of vector databases, embeddings, and retrieval pipelines - Experience with Kubernetes and cloud-native stacks - Prior work on data platforms, AI platforms, or internal PaaS is beneficial. As a Senior Data Engineer, you will be responsible for designing and building core components of the Agentic AI platform, developing scalable data pipelines, contributing to platform abstractions, integrating various data sources, and participating in architecture and code reviews. You will own features across the full SDLC and collaborate with other teams to enhance ACPs AI and data PaaS offerings. Your role will involve following open-source-first and cloud-native engineering practices. Key Responsibilities: - Design and build core components of the Agentic AI platform such as agent workflows, memory, and execution runtime - Develop and operate scalable data pipelines supporting LLMs and AI agents - Contribute to building platform abstractions for secure agent execution - Integrate LLMs, vector stores, and enterprise data sources into reliable systems - Participate in architecture, design, and code reviews for platform services - Own features across the full SDLC including design, development, testing, deployment, and production support - Collaborate with infra, platform, and AI teams to evolve ACPs AI and data PaaS offerings - Follow open-source-first and cloud-native engineering practices Qualifications Required: - 68 years of experience in backend or data engineering - Strong hands-on experience building scalable distributed systems - Proficiency in Java (preferred) or other backend languages like Python, Scala, or Go - Solid fundamentals in data engineering (batch & streaming pipelines), distributed systems, microservices, APIs, messaging, and event-driven architectures - Practical exposure to LLMs and AI-driven systems - Experience across end-to-end SDLC, including CI/CD and production operations - Strong problem-solving and system design skills Additional Details: - Exposure to Agentic AI concepts or frameworks like LangChain, LangGraph, LlamaIndex, AutoGen, etc. is a plus - Experience with public cloud platforms such as AWS, GCP, Azure - Knowledge of vector databases, embeddings, and retrieval pipelines - Experience with Kubernetes and cloud-native stacks - Prior work on data platforms, AI platforms, or internal PaaS is beneficial.