Vetted ETL Professionals

Harika M

Screened

Mid-level Full-Stack Developer specializing in FinTech and enterprise platforms

Plano, TX6y exp

“Engineer with a pragmatic, production-focused approach to AI-assisted development, using tools like Copilot and ChatGPT to accelerate coding while maintaining strict validation for correctness, security, and performance. Particularly notable for building a multi-agent incident-resolution workflow for a financial platform, with specialized agents for log analysis, root cause identification, fix suggestions, and test generation.”

“ML engineer at CGI who built demand forecasting models end-to-end, from feature engineering and training through AWS deployment. Stands out for a production-first mindset and strong skepticism of AI-generated code, including catching a Copilot-generated SQL query that would have caused a costly full table scan in production.”

Yogita Adari

Screened

Mid-level AI Engineer specializing in generative and multimodal systems

San Francisco, CA4y exp

“Built and productionized an agentic LLM automation system for an insurance client to determine medication eligibility, using prompt-chaining plus a RAG pipeline over policy rules and deploying on AWS (Lambda/Step Functions, Bedrock) with a serverless architecture. Addressed major data/schema mismatch issues via a semantic matching pipeline and validated performance through human agreement scoring, A/B testing, KPI monitoring, and confidence-based human-in-the-loop review.”

“Backend-leaning full-stack engineer who has shipped both enterprise workflow software and AI-powered document intelligence products. Stands out for combining practical product judgment with strong production debugging skills across Spring Boot, GraphQL, FastAPI, vector search, and RAG systems, and for improving adoption by making AI search experiences intuitive for non-technical users.”

Alex Guzman

Screened

Principal Software Engineer specializing in cloud infrastructure and identity systems

Denver, CO17y exp

“Senior backend-leaning engineer with experience spanning an 8-person early-stage startup and production microservices environments. They helped deliver an MVP for a fintech/charitable donations product by building a resilient abstraction API across multiple rewards vendors, and more recently maintained Go services at scale with Kubernetes autoscaling for workloads reaching roughly 1 million requests per day.”

“Robotics software/ML engineer who built perception and navigation-related ML systems for autonomous supermarket carts, including object detection, shelf recognition, and obstacle avoidance. Strong ROS/ROS2 practitioner who optimized real-time performance (reported 50% latency reduction) and deployed containerized ROS/ML pipelines at scale using Docker, Kubernetes, and CI/CD.”

“Backend/data platform engineer who owned end-to-end production services for a fleet analytics/GenAI platform, spanning FastAPI microservices on Kubernetes and AWS (EKS + Lambda) event-driven workloads. Strong in reliability/observability (OpenTelemetry, circuit breakers, idempotency), data pipelines (Glue/Airflow/Snowflake), and measurable performance/cost wins (SQL 10s to <800ms P95; ~30% compute cost reduction).”

“AI/LLM practitioner at iFrog Marketing Solutions who drove a RAG chatbot from prototype to production in a legacy, AI-resistant environment by validating customer needs and building a business case. Implemented production-grade LLM practices (CI/CD eval gating, rollbacks, prompt/context engineering) and led internal workshops to bring non-AI-native developers up to speed while partnering with sales on tailored demos to drive adoption.”

“GenAI/ML engineer with production experience at Cognizant and Ally Financial, building end-to-end LLM/RAG systems and ML pipelines. Delivered a domain chatbot trained from 90k tickets and 45k docs, improving intent accuracy (65%→83%), scaling to 800+ concurrent users with 99.2% uptime and sub-150ms latency, and driving +14% customer satisfaction. Strong in Azure ML + DevOps CI/CD, Dockerized deployments, and explainable/PII-safe modeling using SHAP/LIME to satisfy stakeholder trust and GDPR needs.”

“Cross-functional operator who regularly leads globally distributed work and acts as a bridge between product, UX, and analytics. Has driven reporting/dashboard and workflow automation initiatives with senior leadership, using data-backed communication and quick wins to improve adoption and efficiency.”

“Software engineer with experience across data engineering and backend/platform work: owned a Databricks/PySpark real-time pipeline powering customer dashboards with a 15-minute SLA, and helped modernize an investor web app from JSP to React/TypeScript with API + SQL/materialized-view performance improvements. Also contributed to breaking a Java monolith into microservices (Redis + gRPC on AWS EKS) and built an EC2-deployed Play Store/App Store crawler that reduced third-party data costs.”

“JavaScript/Node.js engineer with open-source contribution experience (Mongoose) focused on connection pooling, test reliability, and memory/resource management. Has diagnosed and fixed real-world performance issues in an insurance claims application and improved resilience via failover DB design. Also experienced producing compliance/governance documentation for an EU-based biopharma, enabling stakeholders to make decisions quickly amid changing regulations.”

“Backend-leaning full-stack engineer who ships AI-enabled products end-to-end: built CodeChat, a production internal codebase Q&A tool using RAG with Pinecone and a model-agnostic wrapper across OpenAI/Anthropic/AWS Bedrock, cutting AWS costs ~50% and latency ~45%. Also built and operated RealityStream, a Flask-based real-time forecasting API with JWT/RBAC, MLflow model versioning, and Prometheus/Grafana observability, including handling a real production latency incident via rollback, preloading, and caching.”

“ML/NLP engineer with experience at Charles Schwab building an NLP + graph (Neo4j) entity-resolution system to unify fragmented user/device/transaction data and improve downstream model quality and analyst querying. Has applied embeddings (SentenceTransformers + FAISS) with domain fine-tuning to boost hard-case matching recall by ~12% while maintaining precision, and has a track record of hardening scalable Python/Spark pipelines and productionizing fraud models via A/B tests and shadow-mode monitoring.”

“Financial data engineer who has built and operated high-volume batch + streaming pipelines (200–300 GB/day; 5–10k events/sec) using AWS, Spark/Delta, Airflow, Kafka, and Snowflake, with strong emphasis on data quality and reliability. Demonstrated measurable impact via 99.9% SLA adherence, major reductions in bad records/nulls, MTTR improvements, and significant latency/runtime/query performance gains; also built a distributed web-scraping system processing 5–10M records/day with anti-bot and schema-drift defenses.”

“Data engineer with Cigna experience building and operating an end-to-end AWS-based healthcare claims pipeline processing ~2TB/day, using Glue/Kafka/PySpark/SQL into Redshift. Strong focus on data quality and reliability (schema validation, monitoring/alerting, retries/checkpointing/backfills), reporting improved accuracy (~99%) and reduced latency, plus experience serving real-time Kafka/Spark data to downstream analytics with documented data contracts.”

“Built and shipped production RAG-based LLM agents automating multi-step document query workflows, emphasizing reliability via monitoring, retries, structured exception handling, and fallback retrieval (alternative embeddings/keyword search). Demonstrated measurable gains (18% latency improvement, 25% retrieval efficiency, 12% precision) and has experience integrating agents with messy tax and transaction data at RSM using validation/cleaning and idempotent design.”

“Data engineer focused on AWS + Spark/Databricks pipelines, including an end-to-end nightly loan-data ingestion flow (~2.2M records) from Postgres/S3 through Glue and Databricks into a DWH with layered validation and alerting. Also built real-time streaming with Kafka + Spark Structured Streaming and a master’s project streaming Reddit data for sentiment analysis under ambiguous requirements and tight budget constraints.”

Harideep Balusa

Screened

Mid-level AI/ML Engineer specializing in FinTech risk, fraud detection, and GenAI/RAG systems

USA6y exp

“Built and productionized Azure-based LLM/RAG systems for regulatory/compliance use cases, including automating analyst research and compliance report generation across large unstructured document sets. Demonstrates strong practical depth in hallucination mitigation, hybrid retrieval tuning (BM25 + embeddings), and production MLOps (Databricks, Cognitive Search, AKS, Airflow/MLflow), plus proven ability to deliver auditable, explainable solutions with non-technical compliance teams.”

Ojasmitha Pedirappagari

Screened

Mid-level AI Engineer specializing in LLMs, RAG, and agentic platforms

Jersey City, NJ5y exp

“Built and shipped a production RAG-based assistant that lets parents ask natural-language questions about their child’s learning progress, using pgvector retrieval (child-id filtered) and Redis caching to hit ~180ms latency. Implemented real-world guardrails and compliance (Llama Guard, COPPA, retrieval thresholds, fallbacks) with 99.5% uptime, and ran human-in-the-loop eval loops that improved satisfaction from 3.8 to 4.2 while serving 60k+ monthly users and reducing costs significantly.”

“Worked on industrial pump analytics at SitePro, where they built an anomaly detector using messy sensor and pump data and used historical failure and maintenance cost analysis to make the business case to stakeholders. They combine SQL/Python data preparation with practical stakeholder communication around metrics like churn and operational impact.”

“Analytics candidate currently at Facteus with hands-on experience turning messy transactional data into trusted reporting layers in Snowflake and Power BI. They combine SQL and Python automation with strong validation, performance tuning, and stakeholder-facing metric design, including cohort-based retention and segmentation work that improved trust and adoption of analytics.”

“Healthcare analytics candidate with hands-on experience turning messy EHR, billing, and operational data into validated SQL datasets and automated Python/Airflow pipelines. They appear strongest in hospital KPI reporting—especially length of stay, readmissions, retention, and bed utilization—and have owned projects from metric definition through Power BI delivery and impact measurement.”

Cia Hang

Screened

Executive IT and Operations leader specializing in digital transformation and security

Chicago, IL13y exp

“Candidate is very familiar with the venture capital and broader investment landscape, but is not interested in founding a company. They have worked with several TPG-backed or TPG-owned organizations, helping drive business scaling, cost reduction, and execution against investor governance requirements.”