Open Opportunities
Array
AI Architect
About The Position
Company Overview
Cellebrite’s 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.
Position Overview
As the AI Architect for Cellebrite’s SaaS Platform & Products, you will define and drive the end-to-end AI architecture that powers our cloud AI platform and product capabilities, while ensuring strong alignment with security, privacy, reliability and cost-efficiency requirements. You will partner closely with engineering leaders, product management, data science, security and operations to translate business needs into a scalable, governed and AI platform that accelerates innovation across multiple product lines.
This is a hands-on architecture role: you will set technical direction, run architectural reviews, and build prototypes to validate technology choices and de-risk delivery.
Key Responsibilities
- Own the AI Vision: Take end-to-end ownership of the AI architecture across Cellebrite, including our cloud AI platform, agentic AI infrastructure, and on-prem/hybrid AI deployments.
- Architect an AI Platform for SaaS Products: Define reference architectures and shared building blocks (e.g., AI gateways, orchestration runtimes, memory/RAG systems, vector search, evaluation frameworks, observability, and guardrails) that can be adopted consistently across products and teams.
- Cost Optimization and Scalability: Provide architectural leadership to ensure AI systems are designed for cost efficiency and scalability, actively identify and implement opportunities to optimize cloud spend (FinOps), improve utilization, and meet latency/SLO targets.
- Product-Centric Collaboration: Collaborate with engineering, product, data science, security, and operations teams to translate product requirements and business needs into a strategic AI architecture that drives measurable product value.
- Technology Evolution and Prototyping: Evaluate new technologies, tools, and methodologies to continuously improve our AI systems, build prototypes and proof-of-concepts to validate approaches and accelerate decision-making.
- Technical Reviews, Standards and Mentorship: Lead technical design reviews and provide guidance on best practices and emerging technologies. Act as a technical authority and mentor, fostering a culture of engineering excellence and pragmatic delivery.
- AI Governance, Privacy and Security: Ensure AI capabilities comply with data privacy, security, and ethical AI guidelines. Partner with security and legal stakeholders to implement governance controls and risk mitigations appropriate for regulated environments.
- MLOps and LLMOps Guidance: Mentor teams on operational best practices including model lifecycle management, release processes, monitoring, evaluation, incident response, and continuous improvement.
- AI Productivity Evangelism: Lead the organizational adoption of AI tools that improve engineering productivity and quality (e.g., assisted coding, testing, documentation, and operational automation), while ensuring responsible use.
- Technology Partnerships and Vendor Evaluation: Evaluate vendors and strategic partnerships (models, tooling, observability, vector databases, infrastructure) and provide clear build/buy/partner recommendations aligned to product strategy and total cost of ownership.
Requirements
Minimum Qualifications
- 8+ years of experience in software engineering / platform engineering building distributed, production-grade systems, including 3+ years in an architecture, tech lead, or principal engineer role spanning multiple teams.
- 4+ years of hands-on AWS experience designing, building, and operating cloud-native services (networking/VPC, IAM, compute, storage, observability, and security fundamentals).
- 5+ years of hands-on Python experience in production environments (services, tooling, data/ML pipelines) with the ability to build prototypes and reference implementations.
- 3+ years delivering AI/ML systems into production (reliability, monitoring, evaluation, and lifecycle management).
- 2+ years building GenAI/LLM solutions in production (RAG, tool/function calling, agentic patterns) with a strong focus on safety, quality, and cost.
- Demonstrated experience owning architectural decisions end-to-end, aligning stakeholders, and driving adoption through standards, reference architectures, and enablement.
Core Skills & Experience
- Deep expertise in AWS architecture for cloud-native, multi-tenant SaaS platforms (distributed systems, high availability, resilience).
- Proven experience designing and delivering production-grade AI/LLM systems at scale (reliability, latency, cost).
- Deep knowledge of agentic AI infrastructure, including orchestration runtimes, memory systems, vector databases, AI gateways, MCP/A2A patterns, observability, and guardrails.
- Strong understanding of agentic AI orchestration patterns and tooling, including prompt/tool management and safety patterns.
- Experience with model serving and inference at scale (deployment strategies, routing, caching, batching, autoscaling, GPU/CPU trade-offs).
- Experience monitoring and operating AI systems in production, including SLOs, incident management, and operational excellence practices.
- Strong understanding of data modeling, data quality, lineage, and building reliable retrieval pipelines (RAG) and indexing strategies.
- Strong understanding of MLOps/LLMOps practices (governance, pipelines, experiment tracking, evaluations, release management).
- Experience designing evaluation and testing frameworks for LLM/agent systems (regression, red teaming, online/offline evaluations).
- Excellent communication and interpersonal skills, capable of influencing and aligning stakeholders at all levels, from engineers to business leaders.
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, or equivalent practical experience.
Preferred Qualifications
- AI governance experience in regulated industries (e.g., public sector, enterprise security, or privacy-sensitive domains).
- Background in digital investigations, cyber, public safety, or adjacent domains.
- Experience with Kubernetes (EKS), infrastructure-as-code (e.g., Terraform), and CI/CD for ML/AI workloads.
- Experience implementing data privacy and security controls for AI systems (e.g., access controls, auditability, data minimization, retention policies).
- Experience designing hybrid/on-prem or air-gapped deployments, including secure update and model distribution patterns.
- FinOps experience for AI workloads (GPU capacity planning, cost attribution/chargeback, and cloud spend optimization).
What Success Looks Like
- A clear, actionable AI reference architecture adopted across SaaS products, enabling faster delivery and safer reuse.
- Reliable, observable AI services that meet agreed latency, quality, and availability targets in production.
- Measurable cloud cost optimization through architectural improvements and platform standardization.
- Consistent evaluation and governance practices that reduce risk and improve AI quality over time.
- High-quality technical decision-making and mentorship that elevates the engineering organization.