Vetted System Design Professionals

“Built an autonomous decision analytics LLM agent for end-to-end tabular binary classification, using RAG (FAISS) to retain context across multi-step queries. Deployed as a FastAPI service with production-style reliability features (schema-aware validation, fallbacks, retries, structured outputs) plus offline/online evaluation and monitoring to reduce analysis time and improve consistency versus stateless approaches.”

“Data engineer/backend engineer with experience in healthcare (Cardinal Health provider enrollment) and finance (Northern Trust) building and stabilizing data pipelines and REST services. Worked with APIs and Kafka at ~200k–300k records/day, improving data quality (DLQ + validation), performance (SQL/indexing), and reliability/observability (logging, alerts, consumer lag metrics), and stood up an early-stage financial data service with Jenkins-based CI/CD.”

Sri Lalitha

Screened

Senior Full-Stack Java Engineer specializing in cloud-native microservices and FinTech

California, USA6y exp

“Backend engineer who owned a Python task management API with JWT auth, async notifications, and performance work (DB optimization/caching) to handle high volumes. Led an on-prem to Azure private cloud migration at Morgan Stanley using GitOps and IaC (Terraform/ARM) with phased rollout and rollback planning. Also built a Kafka real-time streaming pipeline with exactly-once/idempotent consumers and Prometheus/Grafana monitoring.”

Ashwitha Reddy

Screened

Mid-level Full-Stack Software Engineer specializing in Java/Spring microservices and AWS

Ohio, United States3y exp

“Backend/platform engineer who has owned a real-time business analytics dashboard backend (Python/Flask/MongoDB) and built Kafka event-streaming pipelines with idempotent processing and DLQs. Strong DevOps/GitOps experience deploying containerized microservices to AWS EKS with CI/CD (Jenkins/GitHub Actions/CodePipeline) and ArgoCD auto-sync/drift detection, plus hands-on support for phased hybrid cloud/on-prem migrations using feature flags and replication.”

Bobby Sanders

Screened

Mid-level Game Designer specializing in systems and gameplay

Anaheim, California6y exp

“Game QA tester/designer hybrid with hands-on Unreal Engine 5 technical debugging experience, focused on NPC AI navigation and performance. At Lantern Tower Games (Telmahre), owned end-to-end quality for an NPC system (dynamic navmesh, moving platforms, EQS, actor pooling), using Unreal Insights and asset reference refactors (hard->soft + async loading) to resolve critical memory/performance issues while collaborating closely with engineering and level design in Scrum.”

Yijun Chen

Screened

Senior Full-Stack Software Developer specializing in IoT and cloud systems

Toronto, ON4y exp

“Frontend-focused engineer who built a full movie recommendation system from concept to production, comparing classic collaborative filtering with LLM-based recommendation approaches on AWS. Emphasizes scalable architecture, strict TypeScript data contracts, and high-quality Next.js/React UI patterns (defensive states, scoped state management, performance optimization) with disciplined QA and feature-flagged rollouts.”

Jake Lee

Screened

Junior Full-Stack/Systems Engineer specializing in AI, embedded systems, and healthcare apps

Boston, MA3y exp

“Led architecture for “Solstice/Solstis,” a safety-aware, hands-free AI medical assistant that guides users through minor emergencies with a structured, state-machine-driven LLM agent integrated with device hardware. Built RAG grounded in Red Cross procedures plus guardrails, fallbacks, and emergency escalation, and improved real-world usability by shifting from open-ended chat to a deterministic step-by-step workflow measured via completion rate, repeat prompts, and latency.”

“Has worked across multiple startup stages from pre-funding through Series D and emphasizes rigorous idea validation through direct conversations with both end users and purchasing decision-makers. Interested in applying NLP to automate summarization/abstracting of highly technical articles, with a balanced view of entrepreneurship that prioritizes health and family.”

Saumay Killa

Screened

Mid-level Full-Stack Engineer specializing in AI SaaS and FinTech

New York, NY3y exp

“Built a career platform feature end-to-end that generates tailored resumes and cover letters using a React/TypeScript frontend, Postgres, and AWS Lambda/SQS backend. Strong in event-driven, serverless architecture and pragmatic product iteration, with a quantified 60% improvement in onboarding completion after redesigning the UX with resume parsing and a multi-step flow.”

“Backend engineer with hands-on ownership of banking microservices from initial design to launch and production support, including security, CI/CD, observability, and incident response. Stands out for measurable modernization impact—~35% faster backend processing, ~40% lower query latency, and ~30% better deployment cycles—and for a pragmatic approach to both Kafka-based async systems and controlled LLM integration in enterprise workflows.”

“Built production AI systems in high-stakes domains, including a medical RAG chatbot focused on reducing hallucinations and a document-processing workflow that automated manual PDF extraction. Demonstrates strong end-to-end ownership across backend services, APIs, LLM integration, and iterative reliability improvements based on real usage and failure analysis.”

“Growth-focused B2B SaaS operator with hands-on experience improving enterprise adoption for a cloud governance and FinOps platform. They combine customer discovery, ROI-driven messaging, automation, and funnel instrumentation to improve conversion and handoffs, citing an 18% lift in enterprise adoption and roughly $200K-$3M in influenced pipeline.”

“Built an internal RAG assistant for financial documents using FastAPI, OpenAI APIs, and vector search, improving document search speed and reducing manual effort for the business team. Stands out for a pragmatic approach to AI engineering: uses AI heavily for productivity, but keeps human judgment central and has designed retrieval, validation, and summarization workflows end-to-end.”

“Currently building a DBC (Digital Birth Certificate) agentic AI system to speed root cause investigation for quality issues at their company. They bring hands-on experience designing and leading multi-agent workflows, including orchestrator/root-agent patterns, evaluation agents, clarification agents, and practical guardrails for hallucination, bias, and rate-limit management.”

“Engineering/technology leader who stays hands-on while driving executive-level roadmap execution, with deep experience modernizing cloud-based LIMS/LIS platforms and building AI-driven lab analytics. Led a monolith-to-microservices cloud migration with containerization and CI/CD, and delivered a reported 30% reduction in lab turnaround time while strengthening compliance.”

“Backend-leaning product engineer with DevSecOps depth who has shipped real-time, Kafka-driven data pipelines and AI-enabled customer-facing features to production on AWS. Built a Spring Boot API layer serving real-time predictions at 100K+ requests/day, improving latency by 35% and user task completion by ~25%, and delivered a React/TypeScript dashboard plus a Postgres audit/history model optimized for search and large event volumes.”

“Engineering Manager/player-coach leading Data Infrastructure, ML/DS, and AI Engineering pods who recently shipped multiple production agentic GenAI features. Built privacy-preserving LLM workflows (PII redaction via Microsoft Presidio) and drove an AI expense-approval agent from ambiguous ask to GA, cutting approval time from ~2.5 days to <4 hours with >85% accuracy. Also owned a major LLM cost overrun incident and implemented cost observability plus circuit breakers to prevent runaway agent loops.”

“Data engineer focused on reliable, scalable analytics pipelines and external data collection. Has owned end-to-end pipelines processing 5–10M records/day, serving Snowflake data marts to Power BI/Tableau, and reports ~99% reliability through strong validation/monitoring. Also shipped versioned REST APIs for curated data with query optimization and caching.”

SNEHA KUSUMA

Screened

Mid-level Java Full-Stack Developer specializing in banking and telecom platforms

Dallas, TX5y exp

“Frontend-focused engineer with experience at T-Mobile and U.S. Bank who maintained a TypeScript utility library (types, tests, build pipeline, and docs) adopted by multiple teams, and improved React workflow performance by refactoring components and optimizing data fetching. Known for pragmatic cross-team support—reproducing issues quickly, shipping well-tested fixes, and managing changes carefully to avoid breaking downstream apps.”

Hamidreza Lotfalizadeh

Screened

Mid-level AI/ML Engineer specializing in LLM agents, RAG, and ML systems

Bay Area, CA6y exp

“At Inertia Systems, built a production LLM-powered ingestion pipeline that converts heterogeneous sources (PDF/JSON/IFC/SQL and financial tables) into standardized text and uses GraphRAG to construct a knowledge graph with verified dependency relationships. Also has hands-on HPC orchestration experience with SLURM, including creating a custom wrapper process manager to improve resource utilization under restrictive scheduling policies.”

Quentin Veron

Screened

Senior Game Economy Designer specializing in live-ops economies and monetization systems

8y exp

“Game economy designer with experience on live F2P mobile titles including Jurassic World: The Game and Ubisoft’s NFL Primetime Fantasy. Built and tuned virtual currency/market systems end-to-end, using Excel simulations and analytics dashboards to forecast progression, prevent inflation, and iteratively improve engagement, retention, and player satisfaction in live ops contexts. Based in Montreal (East Coast) and seeking ~80k CAD; not able to relocate to the Pacific.”

“Built and shipped a production LLM-driven data harmonization/record-matching pipeline for pharmaceutical datasets, combining normalization, embeddings/vector search, and an LLM validation step. Emphasizes production reliability via guardrails, confidence thresholds, idempotent/retryable stages, and human-in-the-loop fallbacks, with monitoring focused on manual review and error rates to reduce false positives.”

Sanket Mungikar

Screened

Mid-level Software Engineer specializing in backend systems and real-time analytics

California, USA5y exp

“Full-stack engineer at BigCommerce who combines customer-facing deployment ownership with hands-on AI/LLM systems work. Built and launched merchant analytics and predictive inventory workflows using React, TypeScript, FastAPI, Kafka, AWS, and RAG-style architectures, and has real production experience debugging non-deterministic AI issues caused by data pipeline freshness and event-ordering problems.”