AI Platform Engineer

Requirements

• 4+ years of experience in backend engineering, distributed systems, or data platform development.
• Solid understanding of backend fundamentals: APIs, microservices, databases, caching, and cloud-native systems.
• Experience working with data pipelines, ETL workflows, or data processing frameworks.
• Familiarity with LLM concepts such as prompting, embeddings, RAG, grounding, and basic evaluation techniques.
• Hands-on experience with Python and/or TypeScript; exposure to Java or Go is a plus.
• Experience integrating APIs or working with external services in production environments.
• Basic understanding of vector databases, search systems, or information retrieval concepts.
• Strong debugging skills and an interest in performance, reliability, and observability.
• Ability to collaborate effectively with platform, research, and product teams. Strong curiosity about AI systems and a desire to learn modern LLM and agent orchestration patterns.

Nice to Haves

• Experience building small-scale AI or LLM-powered applications.
• Exposure to RAG systems, embeddings pipelines, or vector databases (e.g., Pinecone, Weaviate, FAISS).
• Familiarity with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Understanding of basic ML lifecycle concepts (training, validation, evaluation).
• Contributions to academic, open-source, or experimental AI projects.

What You'll Be Doing

• Build and maintain backend services that provide scalable, reliable access to structured and unstructured data for AI systems.
• Develop and optimize data ingestion, transformation, and preparation pipelines to support model training, fine-tuning, and retrieval workflows.
• Implement and support Retrieval-Augmented Generation (RAG) pipelines, including document processing, embedding generation, and vector database integration.
• Contribute to AI orchestration layers that connect LLMs with tools, APIs, memory systems, and internal data sources.
• Assist in building and maintaining runtime systems for LLM inference, ensuring performance, logging, and monitoring guidelines.
• Implement APIs and microservices that expose AI capabilities securely and efficiently to internal developer teams.
• Help design and manage contextual data flows and session-level state management for AI-driven applications.
• Contribute to evaluation and feedback pipelines that measure model outputs, accuracy, latency, and reliability.
• Work closely with senior engineers to productionize AI prototypes and research into stable, scalable services.
• Participate in debugging, performance tuning, and operational support for AI platform services.

Perks and Benefits

• Opportunity to work at Adobe, an industry leader in empowering creativity through innovative platforms and tools.
• Join a team focused on building scalable, production-grade AI platforms.
• Collaborate with talented individuals across platform, research, and product teams.
• Be part of a company culture that empowers employees to make an impact and fosters innovation.
• Access comprehensive benefits programs and opportunities for growth and development.