Vetted Microservices Architecture Professionals

Geetika Jain

Screened

Mid-Level Software Engineer specializing in Azure AI and full-stack development

Park City, UT6y exp

“Hands-on AI/LLM engineer who built a RAG-based product feature end-to-end, including prompt engineering, safety guardrails, and an automated adversarial + load-testing harness. Diagnosed real production issues (null responses) via Azure logs/metrics and drove an architectural fix by separating model deployments to address token/quota limits. Also runs internal developer enablement through short theory-to-hands-on AI workshops after completing a Microsoft AI certification.”

“Engineering leader with experience at Vertex and Salesforce, combining org leadership with hands-on architecture work. He led a 40-person team through execution and quality improvements, drove legacy modernization and SaaS transition, and launched a 0-to-1 voice capability in CRM while also applying AI to code migration and developer productivity.”

“CTO-minded builder seeking to join a startup; previously created an AI-driven platform that abstracted away DevOps and infrastructure for drug discovery researchers. Emphasizes high-leverage, zero-to-one execution with managed cloud/open-source tooling, and a strong reliability/reproducibility mindset validated against existing scientific pipelines.”

“Recent internship project at Google Workspace building an LLM-driven Python backend pipeline to extract/enrich NLP features from messy customer web domains and integrate them into a Domain Feature Store for personalization and promotions. Also has hands-on Kubernetes/Docker deployment experience for a Digital Signage SaaS backend with GitHub Actions CI, plus strong streaming-systems knowledge (Kafka exactly-once, schema evolution, Flink scaling) and built an information retrieval system handling 30,000+ cases.”

“ML/LLM engineer who built a production RAG system (GPT-4 + FAISS + FastAPI) to deliver fast, grounded answers from proprietary documents, optimizing for sub-200ms latency and high-concurrency scale. Strong MLOps/observability background: drift monitoring with Prometheus + Streamlit, automated retraining via Airflow, Kubernetes autoscaling, and MLflow-managed model lifecycle, plus inference cost reduction through quantization and structured pruning.”

“ML/NLP engineer focused on production-grade data and search/recommendation systems: built an end-to-end pipeline that connects unstructured customer feedback with product data using TF-IDF/BERT, Spark, and AWS (SageMaker/S3), orchestrated with Airflow and monitored for drift. Also has hands-on experience with entity resolution at scale and improving search relevance via BERT embeddings, FAISS vector search, and domain fine-tuning validated with precision@k and A/B testing.”

Jim Glassman

Screened

Executive Technology Leader specializing in Enterprise AI, Cloud Architecture, and Data Platforms

Houston, TX16y exp

“Senior data/technology executive who stays hands-on: currently building a Go micro-kernel orchestration layer for medical AI agents to boost concurrency and enforce HIPAA/PHI controls, achieving 26x throughput on migrated workloads. Has led large-scale transformations across healthcare and financial services, including a 45-day data warehouse rebuild at Elara Caring and a data/ML roadmap at Acelity credited with $230M in annual revenue impact prior to 3M acquisition.”

“CTO-level leader with deep insurtech and cloud security/SaaS experience who has repeatedly scaled global engineering orgs and delivered high-velocity roadmaps. Most recently led Delos Insurance Solutions to launch new homeowners programs for wildfire-prone regions every 3–4 weeks while meeting DOI/SLA requirements, driving $135M GWP and $250M capacity and reaching cash-flow positive. Also led major scalability re-architecture at CloudPassage (Postgres to Cassandra + Kafka) and built a large Estonia-based engineering hub at Cybercube.”