Staff Software Engineer - AI Platform

Description About Cellebrite: Cellebrite’s (Nasdaq: CLBT) mission is to enable its global customers to protect and save lives by enhancing digital investigations and intelligence gathering to accelerate justice in communities around the world. Cellebrite’s AI-powered Digital Investigation Platform enables customers to lawfully access, collect, analyze and share digital evidence in legally sanctioned investigations while preserving data privacy. Thousands of public safety organizations, intelligence agencies and businesses rely on Cellebrite’s digital forensic and investigative solutions—available via cloud, on-premises and hybrid deployments—to close cases faster and safeguard communities. To learn more, visit us at www.cellebrite.com, https://investors.cellebrite.com/investors and find us on social media @Cellebrite. Position Overview: You will work on systems that directly support real-world investigations, where reliability, privacy, and trust are paramount. You will join a team building innovative AI capabilities delivered as scalable cloud services across video, image, text, and audio domains. The technical challenges are substantial: multi-modal AI pipelines, retrieval pipelines, context engineering, strict multi-tenant isolation, high-throughput data flows, and real-time response patterns at scale on AWS. You will be part of a senior team that moves fast, owns services end-to-end, and cares deeply about engineering craft, AI quality, and production readiness. What is Your Mission? We are building the next generation of AI-powered investigator tools at Cellebrite. Our cloud-based AI platform delivers cutting-edge capabilities including multimodal analysis, embeddings, OCR, speech-to-text, face detection, image and text classification, and LLM completions through a scalable serverless architecture. As a Senior Software Engineer on the AI Platform team, you will be part of the core team that designs, builds, maintains, and evolves the shared AI service platform that powers real investigative workflows at enterprise scale. You will help turn advanced AI concepts into production-grade serverless systems, shaping orchestration, retrieval, context, and backend architecture across the platform. Responsibilities: AI Service Development: Design, build, maintain, and evolve AI services across modalities: embeddings, OCR, speech-to-text, face detection, image classification, text classification, and LLM completions. Develop production-grade orchestration flows for LLM-based agents, including tool calling, routing, control loops, and failure handling. Build and improve retrieval pipelines across structured and unstructured data using RAG and adjacent retrieval technologies. Shape context engineering patterns that assemble the right information, instructions, tools, and history for each AI workflow. Data Pipeline & Ingestion: Design and implement scalable, reliable, event-driven data ingestion and processing pipelines. Build fault-tolerant workflows for indexing, enrichment, and retrieval over large, complex investigative datasets. Own search index design, vector retrieval patterns, and query performance at scale. Infrastructure & Platform: Build and maintain high-performance async API services and cloud function handlers. Design multi-tenant database schemas with strict data isolation guarantees. Build and maintain serverless infrastructure using infrastructure-as-code and Docker-based deployments. Drive observability through distributed tracing, structured logging, metrics, and alerting. Maintain and improve internal shared libraries, platform components, and engineering standards for AI services. Collaboration: Work closely with product, data, and frontend engineers to translate requirements into backend and AI architecture. Participate in design reviews, code reviews, and cross-team technical discussions. Mentor junior and mid-level engineers on backend and AI best practices. Requirements 5+ years of backend engineering experience. Deep expertise in at least one backend language with strong fundamentals in async programming, type systems, and clean API design. Hands-on production experience with LLM orchestration, agent runtimes, tool-calling flows, or multi-step AI systems. Experience designing multi-tenant SaaS systems with strict tenant data isolation. Solid understanding of event-driven architecture and distributed system design. A genuine passion for AI engineering and curiosity about fast-moving agentic systems, retrieval methods, and emerging model capabilities. Experience with AWS serverless services: Lambda, ECS, API Gateway, SQS/SNS, S3, OpenSearch, Aurora PostgreSQL, Step Functions, DynamoDB. Nice to Have: Experience building production AI systems using RAG, retrieval pipelines, and related retrieval technologies. Experience with infrastructure-as-code tools such as AWS CDK or Terraform. Experience with Docker-based deployments and container registries. Experience with vector databases, search index design, and retrieval infrastructure. Familiarity with multimodality: video, image, text, and audio processing. Experience working in privacy-sensitive, regulated, or security-critical environments. Experience with Python and its ecosystem: async/await, type annotations, FastAPI or similar, Pydantic, and SQLAlchemy.