Vetted ETL Pipelines Professionals

“Open-source JavaScript contributor focused on data visualization, extending Chart.js/React with custom plugins for real-time streaming dashboards. Designed an end-to-end telemetry pipeline using Apache Kafka and Azure Cosmos DB, optimizing partitioning, batching, caching, and client throttling to keep latency low and support thousands of concurrent users. Demonstrates strong ownership in fast-changing environments, including building full-stack AI applications and ingestion/ETL pipelines at Robotics Technologies LLC.”

“Built an end-to-end Alexa smart-home IoT application controlling a Wi-Fi bulb, including ESP32 firmware (MQTT) and an AWS serverless backend (IoT Core/Device Shadow, Lambda, DynamoDB) with a REST API. Demonstrates strong real-time scalability patterns (streaming ingestion, stateless processing, partition-key design) and full-stack delivery with Spring Boot + React (JWT auth, CORS, data-heavy dashboards).”

“Backend engineer who has delivered large, measurable performance wins (10x throughput, 67% latency reduction) by combining Flask microservices, Redis caching, and AWS autoscaling/observability. Has hands-on depth in SQLAlchemy/Postgres optimization and production scaling pitfalls (cache consistency, connection exhaustion), plus experience deploying real-time ML inference (XGBoost) on AWS Lambda and building secure multi-tenant Kubernetes isolation.”

“Accenture data/ML practitioner who deployed a retail churn prediction and BERT-based sentiment analysis system to production, integrating behavioral + feedback data and operationalizing it with ETL automation, orchestration, and CI/CD. Experienced managing 2TB+ multi-source data, monitoring drift in Databricks, and translating results into Power BI dashboards for marketing teams (including K-means customer segmentation).”

“ML/LLM engineer who has shipped production RAG systems (LangChain + HF Transformers + FAISS) with hybrid retrieval and cross-encoder re-ranking, deployed via FastAPI/Docker/Kubernetes and monitored with MLflow. Also partnered with wealth advisors at Edward Jones to deliver a client retention model with SHAP-driven explanations and a dashboard that improved trust, adoption, and reduced high-value client churn.”

“GenAI/ML engineer who has shipped production agentic systems in highly regulated and high-throughput environments, including an AWS Bedrock-based fraud/compliance workflow at U.S. Bank with PII redaction and hallucination detection that cut investigation time by 50%+. Also built and evaluated RAG and recommendation systems at Target, using RAGAS-driven testing, hybrid retrieval with re-ranking, and SHAP explainability dashboards to align model behavior with merchandising business KPIs.”

“Backend/platform engineer who has owned end-to-end production systems in financial/claims domains, including a transaction analytics microservice platform processing ~10M daily operations and cutting latency from ~150ms to <70ms. Also productionized an LLM-powered monitoring/alerting capability (Llama 3 + FastAPI) with prompt design, guardrails, and production evaluation, and led monolith-to-microservices modernization on AWS using feature flags and parallel runs.”

Jaideep bommidi

Screened

Senior ML Engineer & Data Scientist specializing in LLM agents, retrieval/ranking, and MLOps

Denton, TX8y exp

“Machine Learning Engineer currently at Webster Bank building an enterprise-scale LLM agent for Temenos Journey Manager/Maestro, using RAG-style multi-stage retrieval with FAISS/Pinecone, hybrid dense+sparse search, and LoRA fine-tuning optimized via NDCG/MAP and A/B testing. Previously handled messy incident/telemetry data at Deuta Werke GmbH with deterministic + fuzzy entity resolution, and has strong production data engineering experience across Spark/Hadoop and Python ETL systems.”

Goutami Amara

Screened

Mid-level Full-Stack Java Developer specializing in FinTech and Healthcare platforms

Memphis, TN6y exp

“Software engineer who built internal operations/monitoring dashboards for real-time trading and money-movement systems, emphasizing auditability and rapid iteration. Deep experience with microservices on Azure using Kafka/RabbitMQ, plus strong testing discipline (JUnit/Mockito/Testcontainers, contract/E2E) and observability patterns (correlation IDs, centralized logging, distributed tracing) to reduce incident triage time and improve resilience.”

Aditya Agrawal

Screened

Intern Software Engineer specializing in backend systems and data engineering

1y exp

“Backend/AI engineer who has built and shipped two products: Know Founder (Python/SQL/AWS) scaling to 2,000+ users in the first month, and Unifr (unifr.online), an AI search visibility engine that queries multiple LLMs and turns responses into structured brand insights. Strong in production reliability/performance (Redis caching, indexing, precomputation) and in designing agentic workflows with guardrails, validation, retries, and human escalation.”

Mike Khorrami

Screened

Director-level Engineering Leader specializing in enterprise SaaS and cloud-native platforms

Woodland Hills, CA25y exp

“Engineering leader/player-coach who modernized a legacy C#/SQL Server system to Snowflake + Python on GCP, enabling ~30x scale and supporting hundreds of millions of transactions per day per customer. Strong in architecture tradeoffs (Snowflake vs Databricks), production reliability (New Relic, logging/alerting), and lightweight process improvements like a rigorous Definition of Done and structured PR reviews.”

“Business/data analyst with experience at KPMG and Liberty Mutual, focused on financial reporting, data quality, and analytics automation. Has built SQL and Python workflows for large transaction datasets, reduced manual reporting effort by 15+ hours per week, and translated ambiguous business questions into standardized KPIs and Power BI dashboards used for decision-making.”

Akhila Kannegari

Screened

Mid-level AI/ML Engineer specializing in FinTech and retail ML systems

Alabama, USA4y exp

“ML-focused candidate with strong Wells Fargo experience building production fraud systems and internal GenAI tools for fraud analysts. Stands out for measurable impact in fraud detection—raising recall from 71% to 88%—while also demonstrating hands-on depth across streaming infrastructure, MLOps, LLM/RAG implementation, and Python service architecture.”

Naveena Musku

Screened

Mid-level AI/ML Engineer specializing in agentic AI and LLM systems

5y exp

“Backend engineer focused on productionizing LLM systems: built a FastAPI-based RAG and multi-agent automation platform deployed with Docker/Kubernetes, prioritizing safe execution and reduced hallucinations. Experienced in refactoring monolithic ML services with feature-flagged incremental rollouts, and implementing JWT/RBAC plus row-level security (e.g., Supabase) for secure, scalable APIs.”

“Early-career solutions engineer with experience spanning Dell Technologies and insurance operations, combining enterprise hybrid cloud pre-sales exposure with hands-on AI and API integration work. Completed a master's at Illinois Institute of Technology while building customer-facing experience in technical discovery, POCs, security/compliance discussions, and workflow automation.”

Nicholas Leytem

Screened

Staff Software Engineer specializing in high-throughput retail data systems

Minneapolis, MN12y exp

“Software engineer at a global public growth company since 2015, with experience owning a legacy Java ETL analytics application and helping design and launch a Kotlin/Java data sourcing platform for customer order data. Brings hands-on experience across React, Spring Boot, AWS/Kubernetes deployment, and multi-database architectures, including an inventive pseudo-graph implementation in DynamoDB to support flexible document matching.”

“Backend/platform engineer from Dune Security with strong experience turning messy, fragmented workflows into reusable production systems. They’ve built a shared database abstraction layer, integrated multiple enterprise security platforms into a unified workflow, and shipped AWS Bedrock-powered security insight features with guardrails and human review.”

“Backend/AI engineer who has shipped an LLM-powered enterprise support-ticket agent at Comcast, building a production-grade microservices pipeline (FastAPI, SQS, Redis) with strong observability (OpenTelemetry/Splunk/Prometheus/Grafana) and reliability patterns (async, caching, circuit breakers, idempotency). Demonstrated quantified impact at scale—processing 10k+ tickets/day while improving response SLAs and routing accuracy through evaluation and human feedback loops.”

“Data/analytics-focused engineer who builds end-to-end KPI reporting and validation products used daily by plant leads and leadership to track yield, downtime, and defects. Combines Python/SQL + Power BI data pipelines with strong data-quality practices (automated validation, monitoring/alerts) and has experience designing scalable frontend architecture in TypeScript/React and working in distributed/microservices-style data systems.”

“Backend/data engineer with hands-on production experience across GCP and AWS: built FastAPI microservices on Cloud Run and delivered AWS Lambda + ECS Fargate systems with Terraform/GitHub Actions. Strong in data engineering (Glue/Spark, S3/Redshift) and modernization (SAS to Python/SQL), with proven reliability and incident ownership—including cutting a 20+ minute reporting query to under 2 minutes.”

Narendra R

Screened

Senior Full-Stack Java Developer specializing in microservices and cloud platforms

Dallas, TX7y exp

“Backend engineer focused on scalable Python/Flask services and high-performance PostgreSQL/SQLAlchemy systems, with demonstrated wins like reducing N+1-driven response times to under 200ms and cutting P95 latency below 1s via background queues and caching. Has production experience operationalizing ML models as Dockerized APIs on AWS (S3/Lambda) with monitoring (CloudWatch/ELK), plus robust multi-tenant isolation using JWT-driven tenant context and row-level security.”

Dhriti Kanchan

Screened

Mid-level Data Analyst specializing in healthcare and financial analytics

Texas, USA5y exp

“Analytics-focused candidate with hands-on experience turning messy CRM, e-commerce, payments, and support data into trusted reporting datasets using SQL and Python. They have owned end-to-end churn and retention analytics work, including RFM-based segmentation, dashboard delivery, and metric standardization across sales, marketing, and finance.”

“Built and deployed a production RAG-based internal knowledge assistant that let analysts query company documents in natural language, using LangChain/LangGraph with Pinecone and a FastAPI service for integration. Emphasizes reliability in production through hallucination mitigation (retrieval tuning + prompt guardrails) and measurable evaluation/monitoring (accuracy, latency, task completion, hallucination rate), iterating based on user feedback.”