Vetted ETL Pipelines Professionals

“Built a production B2C behavioral interview system for job seekers using LangGraph/LangChain on AWS Bedrock with Nova models, plus a FastAPI backend and Vercel AI SDK frontend. Stands out for practical agent reliability work: local stress testing, OpenTelemetry-to-Datadog observability, token/cost monitoring, and guardrails to keep conversations on track and resistant to instruction override.”

“Full-stack engineer with Cisco supply-chain and Wipro internal platform experience, focused on customer-facing UI performance and secure backend services. Built a bulk Excel inventory upload feature (Spring Boot/Apache POI) that cut manual effort ~80%, and delivered high-scale Angular/React dashboards with strong reliability/observability (FastAPI, JWT, Docker, AWS, AppDynamics).”

“LLM/agent engineer who has shipped multiple autonomous, multi-step agents to production (document-to-SOP conversion, test generation, code generation) using a custom Python DAG orchestrator with persistent state, tool-calling permissions, and structured outputs (Pydantic/JSON Schema). Demonstrates strong production hardening practices—semantic contracts, golden-dataset prompt regression tests, circuit breakers, and multi-level monitoring—and delivered large productivity wins (34 hours of manual writing reduced to ~20 minutes review; ~15–20 engineering hours/week saved).”

Vagmin Yadav

Screened

Junior Software Engineer specializing in backend systems, ML pipelines, and DevOps

Pune, India2y exp

“TypeScript backend engineer in the robotics domain with hands-on experience building low-latency (20–40ms) production systems using RabbitMQ, Redis, and HA PostgreSQL (Patroni). Has owned end-to-end services supporting 15 clients via config-driven architecture, with strong CI/CD, automated testing, and observability (OpenTelemetry) practices, plus API versioning/deprecation using Keycloak auth.”

“Candidate combines fraud analytics experience at Citi with a clinical AI capstone involving reproducible ML pipelines for imaging and notes data. They stand out for turning messy, high-volume data into decision-ready reporting, automating evaluation workflows, and translating analytics into operational impact—from fraud rule changes to retention metric adoption.”

“Built and owned a production conversational AI platform for a healthcare contact center, including RAG-based agent assist, hybrid retrieval, safety guardrails, and production monitoring. Stands out for combining LLM product delivery with strong operational rigor, driving a reported 25-30% improvement in handling time in a sensitive healthcare environment.”

“Built and launched a production self-healing MLOps agent that autonomously diagnosed and fixed model training failures on Kubernetes GPU infrastructure. Combines deep AI infrastructure knowledge with full-stack product ownership, and has delivered measurable impact including 35% less infrastructure waste, nearly 50% less troubleshooting time, and 60% lower LLM API costs.”

“Search/backend engineer with hands-on experience improving Apache Solr-based search systems end-to-end (indexing strategy changes, ETL updates, and Java/Spring Boot Search API work). Demonstrated production rigor with QA partnership, A/B testing, and rollback-safe kill switches, plus measurable product impact (e.g., +1.5% add-to-cart) and operational troubleshooting including traffic/security mitigation.”

“Backend/LLM engineer who built a production-critical Amazon Bedrock + RAG correction and compliance layer for employee communications, integrating tightly with existing Spring Boot/AWS microservices to reduce manual review while keeping outputs explainable and auditable. Also designed an event-driven system processing 10M+ events/day (SQS/Lambda/DynamoDB/Elasticsearch) and handled on-call incidents with strong observability and reliability patterns (idempotency, retries, hotspot mitigation).”

“Built and shipped a production enterprise RAG knowledge assistant that returns grounded, cited answers and uses confidence-based fallbacks (clarifying questions/abstention) with monitoring and compliance controls for sensitive data. Implemented end-to-end agent orchestration (function calling, structured JSON, state, retries/rate limits) plus eval/feedback loops, and achieved a reported 30–40% improvement in knowledge-task completion time while reducing hallucinations via retrieval improvements.”

“Backend/data engineer focused on compliance and statistical processing systems on AWS, building containerized FastAPI services plus event-driven async workflows (Step Functions/EventBridge) with strong reliability patterns (JWT auth, idempotency, structured logging). Has modernized SAS-based batch pipelines into modular Python/AWS services with parallel-run parity validation, and has demonstrated measurable SQL performance wins (40+ min to <10 min) and hands-on incident ownership using CloudWatch-driven detection and prevention.”

“Built and shipped a production LLM-powered RAG assistant for enterprise internal document search (PDFs, knowledge bases, structured data), addressing real-world issues like noisy documents, hallucinations, and latency with grounded prompting, retrieval-confidence fallbacks, and performance optimizations. Also partnered with compliance and business teams at JPMc to deliver a solution aligned with regulatory constraints, supported by monitoring, feedback loops, and systematic evaluation.”

“AI/ML engineer (Cognizant) who built a production, real-time credit card fraud detection platform combining deep-learning anomaly detection with an LLM-based explanation layer. Strong focus on regulated deployment: addressed class imbalance and feature drift, and added guardrails (SHAP/structured inputs, fine-tuning on analyst reports, rule-based validation) to keep explanations accurate and compliant. Orchestrated the full pipeline with Airflow + Databricks/Spark and used MLflow/Prometheus plus A/B and shadow deployments for measurable reliability.”

“Backend/data-systems SWE (2 years) who has built production ETL/streaming workflows (Kafka, Debezium, Elasticsearch) and troubleshot real SQL performance regressions caused by indexing/type issues. Also ships full-stack personal projects in Next.js App Router + TypeScript with Postgres, emphasizing reliability via constraints, idempotency, and strong observability (Grafana/Kibana).”

“Analytics-focused candidate with hands-on experience building SQL reporting tables from messy transactional and master data, plus Python workflows that automate monthly analysis and data checks. They appear strongest in KPI/reporting ownership, metric standardization, and stakeholder alignment, with examples of improving reporting consistency, surfacing issues earlier, and reducing manual reconciliation effort.”

“ML/AI engineer with strong end-to-end production ownership across predictive ML and Generative AI use cases. They built a churn prediction platform that cut churn 12% and preserved about $1.2M in annual revenue, and also shipped a RAG-based support assistant that reduced ticket resolution time 30% while improving agent satisfaction and onboarding speed.”