Vetted AWS Step Functions Professionals

“Backend/data engineer from ASML who modernized a legacy SAS-based statistical processing system into a cloud-native AWS platform (Lambda/FastAPI, Step Functions/EventBridge, Glue, S3/RDS) with strong reliability and data-quality practices. Demonstrated measurable performance wins (RDS query reduced from 90+ seconds to <5 seconds) and hands-on incident ownership for production ETL pipelines.”

“Backend/data engineer with hands-on production experience across Python/FastAPI services and AWS (Lambda, API Gateway, SQS, ECS) delivered via Terraform and GitHub Actions. Built Glue-to-Redshift ETL pipelines with Step Functions retry/catch patterns, schema evolution safeguards, and data quality checks; also modernized a legacy SAS monthly reporting system into Python microservices with rigorous side-by-side parity validation. Demonstrated strong SQL tuning skills with a reported improvement from 5 minutes to 15 seconds.”

“Backend engineer with experience at Amazon building an API-driven service (APS) for large-scale prompt optimization jobs using AWS Step Functions, Batch/Fargate, DynamoDB, and S3, emphasizing idempotency, observability, and secure execution boundaries. Also led a multi-tenant enterprise policy/configuration backend refactor at MAMIT Cyber with versioned schemas, shadow writes, feature-flagged rollout, and PostgreSQL RLS-based tenant isolation.”

“Built a production LLM/RAG assistant for insurance/health claims agents that ingests 100–200 page patient PDFs via OCR (migrated from local Tesseract to Azure Document Intelligence) and delivers grounded claim detail retrieval plus summaries with PII/PHI guardrails. Experienced orchestrating large workflows with Celery worker pipelines and AWS Step Functions (S3-triggered, Fargate-based batch inference/accuracy aggregation), and collaborates closely with non-technical SMEs (claims agents/nurses) through shadowing, iterative demos, and SME-defined evaluation.”

“LLM/agent engineer who has shipped production RAG chatbots in sustainability-focused domains, including a packaging recommendation assistant that standardized messy user inputs and used Pinecone-backed retrieval over product/regulatory data. Experienced orchestrating end-to-end ML workflows with Airflow and AWS Step Functions/Lambda, emphasizing reliability (property-based testing, circuit breakers, OpenTelemetry) and measurable performance (latency/cost). Partnered closely with non-technical leadership to ship 3 weeks early, driving adoption by 150+ businesses and ~20% reported waste reduction.”

“Built and deployed a production generative AI chatbot at NVIDIA using LangChain + GPT-3 integrated with internal data sources, cutting response time nearly in half and improving CSAT by ~12 points. Also delivered LLM-driven QA tools by fine-tuning Hugging Face transformer models and deploying via an AWS-based pipeline (Lambda/Glue/S3) with orchestration (Airflow/Step Functions), CI/CD, Kubernetes, and monitoring (MLflow/Splunk/Power BI).”

“ML/NLP engineer focused on production GenAI and data linking systems: built a large-scale RAG pipeline over millions of support docs using LangChain/Pinecone and added a LangGraph-based validation layer to cut hallucinations ~40%. Also built scalable PySpark entity resolution (95%+ accuracy) and fine-tuned Sentence-BERT embeddings with contrastive learning for ~30% relevance lift, with strong CI/CD and observability practices (OpenTelemetry, Prometheus/Grafana).”

“Data engineer with experience at Moderna and Block owning high-volume (≈10TB/day) production pipelines on AWS, using Kafka/S3/Glue/dbt/Snowflake with strong data quality and observability practices (schema validation, anomaly detection, CloudWatch monitoring). Also built external financial API ingestion with Airflow retries, throttling/token rotation, and schema versioning, and helped stand up an early-stage biomedical data platform with CI/CD and incident debugging.”

“Software engineer who owned customer-facing, high-traffic TypeScript/React + TypeScript backend systems end-to-end, emphasizing safe velocity through feature flags, staged rollouts, observability, and rollback-ready incremental delivery. Reports shipping more frequently with fewer production incidents and faster recovery due to these guardrails.”

“Software engineer with primary experience in Java and Python who also troubleshoots and optimizes JavaScript/React performance issues. Has handled customer-reported production problems via log-driven diagnosis and backend workflow fixes, and took ownership of simplifying and automating a service region-expansion process through time analysis and process documentation.”