Vetted Amazon Redshift Professionals

“Data engineer focused on building reliable, production-grade pipelines and external data collection systems on AWS (S3/Lambda/SQS/Glue/EMR) using PySpark/SQL, serving curated datasets to Snowflake/Redshift for finance and fraud teams. Has operated a large-scale crawler ingesting millions of records/day with anti-bot tactics, schema versioning/quarantine, and CloudWatch/Datadog monitoring, and also shipped a versioned REST API with caching and query optimization.”

“Data engineer focused on production-grade pipelines and data services: ingests millions of records/day into S3, performs SQL/Python quality validation and PySpark/SQL transformations, and serves curated datasets via Athena/Redshift. Has experience hardening external data collection with retries/rate-limit handling and shipping versioned internal data APIs with backward compatibility, monitoring, and CI/CD in early-stage environments.”

SUMIT MAMTANI

Screened

Mid-level Data Scientist specializing in ML, MLOps, and customer analytics

Tempe, AZ4y exp

“ML/NLP practitioner focused on insurance/claims analytics for a large financial firm, working with millions of fragmented structured and unstructured records. Built production-grade pipelines for entity extraction, entity resolution, and semantic search using Sentence-BERT + vector DB, including fine-tuning with contrastive learning (reported ~15% recall lift) and scalable ETL/containerized deployment on Kubernetes.”

Pravalika Kasojjala

Screened

Mid-level AI/ML Engineer specializing in LLM, RAG/GraphRAG, and fraud analytics

Charlotte, NC5y exp

“LLM/agent engineer who has deployed a production internal assistant to reduce employee inquiry resolution time while maintaining regulatory compliance. Experienced with RAG, hallucination risk triage, and graph-based orchestration (LangGraph) for enterprise/banking-style workflows, emphasizing schema-validated, citation-backed, tool-constrained agent designs and tight collaboration with non-technical business/compliance stakeholders.”

Shashank Nawathe

Screened

Senior Front-End Engineer specializing in React, micro frontends, and GraphQL

Sunnyvale, CA8y exp

“Frontend engineer with Walmart Labs experience who contributed to the React Query (TanStack) open-source ecosystem by reproducing and helping fix a tricky cache invalidation edge case via a detailed GitHub issue and merged PR. Led measurable runtime performance improvements on a large B2B dashboard (D3.js), using memoization, component refactors, web workers, and virtualization to cut render time ~60% and eliminate UI freezes, and standardized data-fetching patterns to reduce data-related bugs ~30%.”

Keerthi Kalluri

Screened

Senior Full-Stack & GenAI Engineer specializing in healthcare and financial services

6y exp

“Built and deployed a production LLM-powered customer support assistant using a RAG backend in Python, focused on deflecting repetitive Tier-1 tickets and reducing resolution time. Demonstrates strong production engineering instincts around reliability (confidence scoring + human fallback), scalability/cost optimization (multi-stage pipelines), and workflow orchestration/observability (LangChain, custom DAGs, structured logging, step metrics).”

Nikitha Margadi

Screened

Mid-level Data Engineer specializing in cloud lakehouse, streaming, and MLOps

Texas, USA5y exp

“Data engineer at AT&T focused on large-scale telecom (5G/IoT) data platforms, owning end-to-end pipelines from Kafka/Azure ingestion through Databricks/Delta Lake transformations to serving analytics and ML. Has operated at very high volumes (~50+ TB/day) and delivered measurable performance gains (25–30% faster processing) plus improved reliability via Airflow monitoring, robust data quality checks, and resilient external data collection patterns (rate limiting, retries, dynamic schemas).”

Krishnamraju Penumatsa

Screened

Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines

Fort Worth, TX6y exp

“Data engineer currently at American Airlines who built and owned end-to-end flight operations and booking data pipelines (batch + real-time) using Azure Data Factory, Kafka, Spark/Databricks, Synapse, and Snowflake—processing hundreds of GBs/day. Strong focus on reliability and data quality (idempotency, checkpointing, retries, validation/alerts) and delivered near-real-time analytics powering Power BI dashboards; previously helped stand up an early-stage data platform at Sysco on AWS (Glue/S3/Redshift) with Airflow and Jenkins CI/CD.”

“Healthcare-focused data analyst with hands-on experience at Molina Healthcare building SQL and Python workflows for retention and churn analytics. They combined enrollment, CRM, and claims data into Power BI reporting, automated predictive churn analysis, and tied their work to measurable outcomes including faster processing, better reporting accuracy, and reduced churn.”

“Data science and AI professional with Capital One experience building churn prediction and GenAI-powered document intelligence solutions. Stands out for pairing hands-on technical depth in NLP, LLMs, and analytics with strong business communication, including driving adoption across teams and contributing to a 25% reduction in customer churn.”

Candida Shaima D'souza

Screened

Senior Full-Stack Engineer specializing in frontend and EV charging platforms

San Jose, CA7y exp

“Built end-to-end operational experiences for Siemens' Depotfinity EV Charging Management System, spanning web, mobile, backend coordination, and cloud-integrated real-time telemetry. Particularly strong in high-frequency React/TypeScript dashboard performance, WebSocket-driven operational products, and cross-functional ownership of fleet-scale EV charging workflows.”

“Built and deployed an LLM-powered customer support assistant (“Notable Assistant”) focused on automating common post-customer queries while maintaining multi-turn context and meeting scalability/latency needs. Experienced with production orchestration and operations using Kubernetes and Apache Airflow (DAG-based ETL, scheduling, monitoring/alerts), and has partnered closely with customer service stakeholders to align chatbot behavior with brand voice through iterative testing.”

“Applied NLP/ML engineer with experience at KeyBank and Novartis building production document intelligence and entity-resolution systems in finance and healthcare. Has delivered end-to-end pipelines (Airflow + AWS) using transformers (DistilBERT/Sentence-BERT), vector search (FAISS/Milvus/Pinecone), and human-in-the-loop labeling to achieve measurable gains (40%+ faster queries; up to 88% F1 and 93% precision/90% recall in entity linking).”

Janvitha Mandyam

Screened

Mid-level AI/ML Engineer specializing in Generative AI and NLP

Chicago, IL4y exp

“GenAI/LLMOps practitioner who deployed a production RAG-based customer service and knowledge retrieval system for a global bank using LangChain, FAISS/Azure Cognitive Search, GPT-4/Claude, and Guardrails—driving a reported 35% Q&A accuracy lift while reducing handle time and escalations. Also partnered with non-technical leaders at CVS Health to deliver ML-driven supply chain risk and inventory insights via anomaly detection, NLG summaries, and stakeholder-friendly dashboards.”

“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.”

“Backend/data engineer with hands-on production experience building FastAPI APIs secured with JWT and delivering AWS-based data processing solutions using Lambda and ECS Fargate. Has worked with Snowflake/third-party API sources and targets including DynamoDB, S3, RDS, Redshift, and Glue, and uses CloudWatch/X-Ray for monitoring and troubleshooting. Seeking ~$65/hr and is open to onsite work in Bethesda, MD.”

“GenAI/RAG engineer who has deployed a production policy/regulatory search assistant for a financial client using LangChain + Vertex AI, FastAPI, Docker/Kubernetes, and Airflow-orchestrated data pipelines. Demonstrated measurable impact with 50–60% latency reduction and 70% fewer pipeline failures, plus KPI-driven grounding evaluation (90%+ target) and strong cross-functional collaboration with compliance/business teams.”

“Built an end-to-end GenAI underwriting copilot at TD Bank for complex financial documents, combining RoBERTa-based risk classification with Azure OpenAI RAG to deliver grounded, citation-based insights. Drove a 40-50% reduction in manual underwriting review time and created reusable FastAPI ML services that cut integration effort for other teams by 30-40%.”