Vetted Batch Processing Professionals

“Backend/AI engineer with experience in both high-scale financial services (JP Morgan trade compliance analytics API on Java/Spring Boot/Postgres/Elasticsearch on AWS EKS processing 1M+ trades/day) and applied LLM systems for legal research (LangChain/OpenAI + Weaviate semantic search). Demonstrated strength in reliability/performance engineering, data consistency during migrations, and production-grade workflow orchestration with observability and human-in-the-loop guardrails.”

“Backend engineer with PayPal experience owning a high-throughput, low-latency fraud detection pipeline processing billions of transactions/day, integrating LLM-based models into real-time Kafka streams and payment decisioning APIs. Strong Kubernetes + GitOps practitioner (declarative, auditable deployments; autoscaling/probe tuning) with migration experience modernizing legacy systems onto AKS/OpenShift.”

“Backend engineer who built an async FastAPI data pipeline at GHN Career Academy to replace a manual Excel-based workflow, migrating 30k+ contact records into Airtable with validation/deduplication and best-effort GPT-based enrichment. Emphasizes reliability under messy real-world data and partial failures via structured logging, retries, and resumable processing, unlocking downstream automations (e.g., Zapier and chatbots).”

“Backend engineer with primary experience in Java/Spring Boot microservices, AWS (EC2/ECS/Lambda), and CI/CD automation with Jenkins. Supported modernization/migration efforts at Berkshire Hathaway and Citius Infotech by containerizing legacy components with Docker, refactoring services to be stateless, and managing infra changes via Terraform and Git-based workflows; has limited but practical Python API prototyping experience (Flask/FastAPI) and solid conceptual grounding in Kubernetes and Kafka.”

“Backend engineer with fintech/banking experience (e.g., Canara Bank) building secure Python/Flask microservices for financial reporting and unified data access. Strong in Postgres/SQLAlchemy performance optimization (including materialized views) and in productionizing ML services on AWS (Lambda/ECS/CloudWatch) with Docker, model registries, and blue-green deployments, plus multi-tenant isolation via JWT-based middleware.”

“AI engineer who shipped a production chat assistant for a storage company by building the underlying RAG-style knowledge base (document ingestion, chunking/embeddings, FAISS vector store) and an admin update interface to keep content current. Also has full-stack delivery experience (Python REST APIs + React/TypeScript UI) and AWS operations using Terraform/Jenkins, including handling a real production performance incident by optimizing DB queries and adding auto-scaling.”

“AI/ML engineer who built a production RAG-based internal document intelligence assistant (LangChain + Pinecone) to let employees query enterprise reports in natural language. Demonstrated hands-on pipeline orchestration with Apache Airflow and tackled real production issues like retrieval grounding and latency using tuning, caching, and token optimization, while partnering closely with non-technical business stakeholders through iterative demos.”

“Data engineer/backend data services practitioner with Bank of America experience building real-time and batch transaction-monitoring pipelines and APIs (Kafka + databases, REST/GraphQL). Highlights include a reported 45% response-time improvement through performance optimizations and use of Delta Lake schema evolution plus CI/CD (GitHub Actions/Jenkins) and operational reliability patterns like CloudWatch monitoring and dead-letter queues.”

“Backend/data engineer with hands-on production experience building reliable Python FastAPI services on Kubernetes and delivering AWS EKS + Terraform CI/CD with strong secrets isolation and rollback practices. Also built AWS Glue ETL pipelines into S3/Redshift with schema-evolution handling and data-quality controls, modernized legacy analytics into modular Python services with parallel-run parity validation, and has demonstrated SQL tuning impact (minutes to seconds) plus ownership of batch pipeline incidents end-to-end.”