Hi S***, I’m J***, an HR recruiter at StrategyBrain. I came across your profile and was impressed by your leadership as System Architect and CTO at C***s,especially your work on AWS-native, microservice-based systems and ML-driven transport solutions. We’re partnering with T***B to fill a Director of Cloud Architecture and AI Solutions role, and your background seems like a great fit. Would you be open to a brief chat to learn more about this opportunity?

Hi S*** , great to hear you’re interested! Here’s a bit more about the role and T***B: T***B is a fast-growing logistics-tech company (200+ employees) focused on AI-driven pricing and matching solutions. We’re looking for a Director of Cloud Architecture and AI Solutions who will: • Lead end-to-end design and implementation of AWS-native, microservice-based systems, ensuring high availability and performance • Define and enforce architecture standards, best practices and CI/CD pipelines across engineering teams • Drive integration of ML models and big-data analytics into our transport pricing, matching and recommendation engines • Mentor and manage a team of system architects and engineers, fostering innovation and knowledge sharing • Collaborate closely with product, operations and business stakeholders to align technology strategy with company goals Why this is a great fit for you: • Career impact: You’ll shape the cloud and AI roadmap at scale, influencing strategic decisions at the executive level • Growth: Significant budget and autonomy to build out tools and processes,plus support for ongoing professional development • Resources: A dedicated team, modern AWS infrastructure, ML tooling and a strong data science group to partner with • Compensation: Competitive salary, performance bonus and equity participation Does this align with what you’re looking for? Do you have any questions about the team, tech stack or career path? If you’d like to discuss further, I’m available for a 30-minute call. Would Wednesday or Thursday morning (CET) work for you? Let me know your preferred day and time. Looking forward to our conversation!

Hi S***, Wednesday at 10:00 CET works perfectly,I’ll send over a calendar invite shortly. At T***x, our Cloud Architecture & AI team is about 15 people strong: • 3 Senior System Architects • 4 Backend Engineers (microservices) • 4 ML Engineers/Data Scientists • 2 DevOps Specialists (CI/CD & infrastructure) • 2 Cloud Infrastructure Engineers Our main ML integration challenges are: 1. Real-time inference at scale for pricing and matching without sacrificing latency. 2. Ensuring data quality and consistent feature pipelines across distributed AWS services. 3. Automating model deployment, versioning and rollback within a robust MLOps framework. 4. Monitoring model drift and orchestrating automated retraining triggers. Your background in AWS-native architectures and ML-driven transport solutions at C***s makes you an ideal collaborator on these fronts. Looking forward to diving into this on Wednesday! Best regards, J*** HR Recruiter, StrategyBrain (partnering with T***B)

Hi S***, Great questions. For our high-throughput pricing and matching calls, we’re targeting sub-100 ms P95 latency and around 200,300 requests/sec at peak. Right now we host most real-time models as AWS SageMaker real-time endpoints, and for a handful of especially high-volume or custom architectures we leverage TensorFlow Serving on EKS. On the MLOps side, we’ve standardized on SageMaker Pipelines and the Model Registry for versioning, A/B deployments and rollbacks. Infrastructure as code (Terraform + CodePipeline) handles endpoint provisioning, while our DevOps team has built custom Lambda hooks to automate rollbacks on metric regressions. We also pull in CloudWatch and SageMaker Model Monitor to detect drift and trigger retraining workflows. Happy to dive deeper into any of these choices on our call Wednesday at 10:00 CET. Looking forward to it!

Hi S*** , great questions. 1. Real-time feature consistency We lean on AWS SageMaker Feature Store for both online (low-latency lookups) and offline (batch backfill) access. All feature pipelines write to a centralized feature registry, and our microservices pull directly from the online store (Redis-backed) to guarantee the same values in training, batch scoring and real-time inference. 2. Rollback vs. manual review Our Lambda rollback hooks watch three key production metrics: • Prediction latency (P95 exceeds our 120 ms threshold) • Model-level error/price deviation (e.g. pricing delta >X %) • Statistical drift (Model Monitor’s PSI or AUC change beyond preset bounds) When any of these breach their alert thresholds, Lambda automatically rolls back to the last approved model. Separately, Model Monitor flags distribution skews, missing features or data freshness lags,which trigger a manual review by our ML Ops team before we push any new deploy. Looking forward to unpacking this in more detail on Wednesday at 10:00 CET. Drop me a note if there’s anything else you’d like on the agenda! Best, J*** HR Recruiter, StrategyBrain (partnering with T***B)

Hi S***, Great questions,happy to give a bit more color before our call: 1. Feature schema evolution & high-throughput writes • We enforce strict versioning in our feature registry (Git-backed) and tag each feature with a major/minor version in SageMaker Feature Store metadata. When a schema changes, our Glue/Spark backfill jobs translate old versions to the new schema, and online reads still honor the version your model expects,so no surprise fields or missing data. • To support 200,300 req/sec writes without bottlenecks, we front writes with a Kinesis Data Stream (dynamically sharded) and ingest into the Feature Store via parallel PutRecordBatch calls. This decoupling means our microservices push asynchronously, we auto-scale the stream shards, and we handle retries with a small Lambda-based backoff loop,keeping latency sub-100 ms and avoiding write throttling. 2. PSI/AUC thresholds & rollback sensitivity • We started with standard drift guardrails (PSI>0.2, AUC drop >0.02) and ran backtests against several quarters of C***s-style pricing data. By correlating drift alerts with business KPIs (revenue per load, bid win rate), we iteratively tuned thresholds to catch real degradations without flagging noise. • Today, we auto-rollback when PSI exceeds 0.15 or AUC delta is worse than ,0.03. Milder breaches (PSI 0.1,0.15 or AUC delta ,0.02 to ,0.03) generate an immediate manual review ticket in Slack. That balance keeps our live models stable while avoiding unnecessary interruptions. Looking forward to walking through the nitty-gritty on Wednesday at 10:00 CET. Drop me a note if there’s anything else you’d like on the agenda! Best, J*** HR Recruiter, StrategyBrain (partnering with T***B)