Vetted Confluence Professionals

“Built and shipped a production agentic LLM tool that helps internal teams update technical product whitepapers using plain-language edit requests, with strong guardrails (citations, verification, refusal/clarify flows) to reduce hallucinations and maintain compliance. Experienced taking LLM workflows from rapid LangChain prototypes to more predictable, debuggable LangGraph agent graphs, and orchestrating end-to-end ingestion/embedding/indexing/eval/deploy pipelines with Kubeflow.”

“Product and consumer growth professional from a B2C app background, focused on improving retention/activation and driving revenue through customer/product data. Experienced in maintaining BI layers and dashboards and running KPI-driven analysis across returns/refunds operations (NPS/CSAT, TAT, repeat complaints). Familiar with core F2P monetization mechanics and how to evaluate IAP offers via A/B testing; has shipped/managed products on mobile and web.”

“Customer-facing technical professional with Amazon incident-management experience who helps drive adoption of complex ML/LLM solutions by delivering hands-on demos and rapid model fine-tuning. Applies a disciplined debugging approach (repro + logs/metrics + severity triage) and maintains runbooks to resolve SEV2 issues in ~1 hour, while also partnering with sales/customer teams to ship patches and new features based on feedback.”

“UE5 gameplay scripter/engineer who worked on player-facing weapon systems for Perfect Dark, owning Blueprint-driven state management, projectiles/damage, and player feedback with a strong focus on edge-case polish and responsiveness. Experienced in modular Blueprint architecture (inheritance-based) for scalable content, playtest/analytics-driven iteration, and performance optimization of gameplay loops (e.g., reducing runtime NPC tracking for a disguise gadget).”

“Strategic enterprise Customer Success leader from Atlassian Cloud managing a >$10M ARR, ~33k-user account end-to-end, driving measurable adoption (+12%), services expansion (+30%), and strong satisfaction (4.5/5). Experienced leading cross-functional deployments of AI agents (Rovo) and Forge-based integrations, and translating enterprise governance needs (e.g., RBAC at scale) into roadmap-shaping product requirements.”

Kaushik Sriram

Screened

Mid-level Software Engineer specializing in event-driven FinTech backend systems

San Francisco, CA5y exp

“Senior/Staff-level backend/platform engineer who owned Stripe’s global payout settlement system end-to-end, building an event-driven Python/Kafka platform processing millions of events daily across 30+ countries. Deep experience operating high-reliability distributed systems in production (incidents, replays/backfills, schema evolution, observability) and scaling on AWS/EKS with strong testing and deployment practices.”

“Built and operationalized an internal LLM/RAG system for engineering specs—starting with an at-home prototype using real ERP documents, then securing hardware, standing up a GPU/software stack, and deploying through UAT to production. Identified organizational gaps (no shared spec repository) and created a queryable RAG database that reportedly cut document discovery from days/weeks to minutes, while also resolving retrieval issues via improved PDF-aware chunking.”

“ML/LLM engineer with Stripe and Adobe experience who productionized a transformer-based Payments Foundation Model for real-time fraud detection at global scale (billions of transactions). Built petabyte-scale ETL/feature pipelines (Spark/EMR, Airflow, dbt, Kafka/Flink) and achieved <100ms multi-region inference (EKS, TorchServe, edge/Lambda, GPU/CPU routing) with strong PCI-DSS/GDPR compliance and explainability (SHAP/LIME), reporting a 64% fraud accuracy improvement.”

“Enterprise Customer Success/Account Manager with deep Braze martech expertise, driving adoption, renewals, and expansion across major consumer brands. Notable wins include growing a nationwide coffee retailer account from $400k to $1.2M, securing a $100k+ TAM upsell through expectation resets, and turning a churn-risk DTC onboarding into a case study and conference speaker via cross-functional execution and product feedback loops.”

“Unity/C# gameplay engineer who has shipped a modular, data-driven combat ability system with strong measurable outcomes (≈80% fewer GC allocations, 15–20% better frame times, 10–12% higher early retention). Also integrated an LLM-driven NPC dialogue/quest hint system with a C#/.NET backend, caching/guardrails, and telemetry-driven iteration, and shipped Photon PUN real-time 4-player co-op plus a shared codebase across Meta Quest VR and iOS/Android.”

“Data science/NLP practitioner with experience at NVIDIA and Microsoft building production-grade NLP and data-linking systems. Has delivered high-performing pipelines (e.g., F1 0.92) and large-scale entity resolution (F1 0.89), plus semantic search using embeddings and Pinecone with ~30–40% relevance gains, backed by rigorous validation (A/B tests, ROUGE, MRR) and strong MLOps/workflow tooling (Airflow, Databricks, FastAPI, MLflow, Prometheus/ELK).”