Vetted Schema Validation Professionals

“Backend/data engineer with deep healthcare data experience (FHIR, de-identification) across both GCP and AWS. Has built and operated production microservices and ETL pipelines (FastAPI, Dataflow, Glue) with strong reliability practices, and led modernization of a legacy SAS compliance reporting system to cloud services with validated parity and stakeholder-facing Looker comparisons.”

“LLM/agent engineer who built a production code-generation agent at Corvic AI that lets non-technical users query CSV/tabular data in natural language by generating and executing Python. Focused on making LLM systems reliable and scalable via schema-aware validation, sandboxed execution-feedback retries, prompt caching/embeddings, async execution, and high-throughput data processing with Polars; also partnered with Adobe product/marketing to ship brand-aligned AI content generation for email and push notifications.”

Muneendra M

Screened

Mid-level Software Engineer specializing in event-driven backend and AI-enabled systems

San Francisco, CA4y exp

“Full-stack engineer at Stripe who owned a webhook monitoring and retry platform end-to-end, spanning backend services, React dashboards, and production operations. Stands out for combining strong distributed-systems judgment with product polish, including a reported 31% improvement in webhook delivery reliability and UI improvements that reduced support burden.”

“Built end-to-end AI analytics experiences spanning React/TypeScript, serverless APIs, and Postgres, with a strong focus on streaming UX, observability, and reliability. Stands out for turning ambiguous AI product ideas into shippable MVPs, then abstracting repeated patterns into reusable orchestration and multi-tenant configuration systems that improved speed, consistency, and maintainability.”

“Backend/AI engineer currently at Stripe building Minions, an internal LLM-based developer agent that automates code generation, bug fixes, and PR creation. They combine strong production LLM architecture skills with reliability engineering, having improved routine PR merge times by ~40-45%, lowered post-merge bug rates by 12%, and previously built an invoice-processing pipeline that achieved 91% straight-through processing in a B2B payments context.”

“McKinsey practitioner who built and deployed production LLM systems for consultants/clients, including a Power BI-integrated multi-agent chatbot (RAG + text-to-SQL + formatting) with custom Python orchestration, verification loops, and a 100+ case eval set achieving ~95% consistency. Also delivered a taxonomy-mapper agent that standardized inconsistent labeling for C-suite stakeholders, cutting a process from >2 weeks to <30 minutes through demos and business-focused communication.”

“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”

“Built and shipped an end-to-end LLM agent during an AT&T internship to automate network troubleshooting, with production-style reliability safeguards (timeouts/retries/fallbacks) and structured, state-machine orchestration; project won 3rd place in AT&T’s nationwide intern innovation challenge and was demoed to leadership. Also handled messy multi-partner data at Tencent by implementing schema validation/normalization, confidence-threshold fallbacks, and idempotent Python/ORM-based pipelines.”

Priyanka Kaswan

Screened

Senior AI Research Engineer specializing in LLM agents and large-scale ML

7y exp

“AT&T Labs builder who deployed a production multi-agent LLM system that lets engineers ask natural-language questions and automatically generates deterministic, schema-grounded Snowflake SQL (200–400 lines) to detect anomalies in massive wireless/network event data (~11B events/day). Experienced with LangChain and Palantir Foundry orchestration, RAG-based result interpretation, and rigorous evaluation/monitoring loops to continuously improve reliability.”

“Serverless-focused backend/data engineer who has delivered production Python services on AWS (FastAPI on Lambda/API Gateway) plus Glue-based ETL pipelines from S3 to relational databases. Strong in operational reliability (timeouts, retries, monitoring/alerts) and modernization work, including parallel-run parity validation for migrating legacy batch logic to Python services. Demonstrated measurable SQL tuning impact (15 min to under 3 min).”

“Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.”