Vetted Azure Data Factory Professionals

“Frontend-focused engineer who built a full movie recommendation system from concept to production, comparing classic collaborative filtering with LLM-based recommendation approaches on AWS. Emphasizes scalable architecture, strict TypeScript data contracts, and high-quality Next.js/React UI patterns (defensive states, scoped state management, performance optimization) with disciplined QA and feature-flagged rollouts.”

“Built and deployed production LLM-powered document summarization/classification systems using embeddings, vector databases (RAG-style retrieval), and automated evaluation (BERTScore/ROUGE), with a focus on monitoring and scalable cloud pipelines. Also partnered with a fraud analytics team to deliver a transaction anomaly detection solution, translating model outputs into Power BI dashboards and actionable KPIs while iterating on thresholds and alerts based on stakeholder feedback.”

“LLM/GenAI engineer with US Bank experience building a production financial-document intelligence platform using LangChain/LangGraph, GPT-4, and Amazon OpenSearch. Delivered a RAG-based assistant for compliance/audit teams with grounded, cited answers, focusing on reducing hallucinations and latency, and deployed securely on AWS (SageMaker/EKS) with CI/CD and evaluation tooling (LangSmith, RAGAS).”

“Healthcare data/ML practitioner with experience at UnitedHealth Group building production ETL and streaming pipelines (Python, BigQuery, Kafka) that unify EHR, IoT device, and lab data for patient risk prediction. Also implemented embedding-based semantic search/linking for noisy clinical notes via domain adaptation and rigorous validation with clinical stakeholders; previously built churn prediction at DirecTV using XGBoost.”

“At KPMG, built and productionized a secure RAG-based LLM assistant that lets business and risk stakeholders query data warehouses in natural language, reducing dependence on data engineers for ad-hoc analysis. Demonstrates strong production rigor (Airflow orchestration, CI/CD, containerization), retrieval/embedding tuning (rechunking, semantic abstraction for structured data), and reliability controls (confidence thresholds, refusal behavior, monitoring and canary evals).”

“GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.”

“Built production LLM systems including a real-time customer feedback analysis and workflow automation platform using RAG and multi-agent orchestration with confidence-based human escalation, addressing privacy and legacy integration challenges. Also automated ML operations with Airflow/Kubernetes (e.g., daily churn model retraining) cutting retraining time to under 30 minutes, and demonstrates a rigorous testing/monitoring approach plus strong non-technical stakeholder collaboration.”

“ML/GenAI engineer with recent CVS Health experience building a production RAG system over unstructured financial/research documents using LangChain, FAISS, and Pinecone, plus LoRA/PEFT fine-tuning of GPT/LLaMA for domain-aware summarization. Demonstrates strong applied MLOps and data engineering skills (Airflow/Prefect, Docker/Kubernetes, CI/CD, MLflow) and measurable impact (sub-second retrieval, ~40% better context retrieval, ~25% entity matching improvement).”

“Healthcare-focused data engineer/ML practitioner with experience at Lightbeam Health Solutions and Humana building production entity-resolution and semantic similarity pipelines across EMR, lab, and claims data. Uses NLP/ML (spaCy, scikit-learn, BioBERT/LightGBM) plus Snowflake/Airflow and vector search (Pinecone) to improve linkage accuracy (reported 90%) and semantic match quality (reported +12–15%), while reducing manual cleanup by 40%+.”

“Cloud/Kubernetes-focused production engineer with experience running 99.95% uptime platforms across AWS/Azure/GCP. Strong in incident response and performance troubleshooting (including a 30% MTTR reduction), and in building secure CI/CD and Terraform-based IaC for AKS/GKE microservices with robust change controls and rollback practices. Notably does not have direct IBM Power/AIX/VIOS/HMC or PowerHA/HACMP ownership.”

“GenAI/LLM ML engineer (currently at Webprobo) building an enterprise GenAI platform with document intelligence and automation on AWS and blockchain. Has hands-on experience with RAG, LLM evaluation tooling, and orchestrating production LLM workflows with Apache Airflow, plus deep exposure to reliability challenges in globally distributed/edge deployments. Also partnered with business/marketing stakeholders at a banking client to deliver an AI-driven customer retention insights solution.”

“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).”

“Built a web-based online Sudoku game in JavaScript (multiplayer format supporting up to 6 teams with up to 5 players each) and demonstrates strong product/analytics orientation. Uses a KPI-driven approach (DAU/WAU, ARPU, session duration, LTV) and structured prioritization methods (MoSCoW, story mapping, cost of delay, DFV) to iterate toward targets; seeking a remote role around $70k/year.”

“Backend/platform engineer who owned a real-time customer analytics microservice stack in Python/FastAPI with Kafka streaming into PostgreSQL, including schema enforcement (Avro) and high-throughput optimizations. Strong Kubernetes + GitOps practitioner (EKS/GKE, Helm, Argo CD) who has handled CI/CD reliability issues with automated pre-deploy checks and rollbacks, and supported major migrations (on-prem to AWS; VM to EKS) with blue-green cutover planning.”

“Built a production, agentic multi-agent pharmaceutical intelligence system for US oncology (breast cancer) conference/news intelligence, automating MSL-style information gathering and summarization for pharma and healthcare stakeholders. Uses CrewAI + LangChain orchestration, custom scraping across ~15 pharma newsrooms, and a grounding-score evaluation approach (sentence transformers/cosine similarity) to mitigate hallucinations.”

“Built and deployed a production LLM-powered clinical note summarization system used by care managers to speed review of 5–20 page unstructured medical records. Implemented safety-focused validation (prompt constraints, rule-based and section-level checks, human-in-the-loop) to reduce hallucinations while maintaining low latency and meeting privacy/regulatory constraints, integrating via APIs into existing clinical tools.”

“AI/LLM engineer with production experience building secure, scalable compliance-focused generative AI systems (GPT-3/4, BERT) including RAG over internal regulatory document bases. Has delivered end-to-end pipelines on AWS with PySpark/Airflow/Kubernetes/FastAPI, emphasizing privacy controls, monitoring, and iterative evaluation (A/B testing). Also partnered closely with bank compliance officers using prototypes to refine NLP summarization/classification and reduce document review time.”

“AI/ML engineer with production experience building a RAG-based internal analytics assistant (Databricks + ADF ingestion, Pinecone vector store, LangChain orchestration) deployed via Docker on AWS SageMaker with CI/CD and MLflow. Strong focus on real-world constraints—latency/cost optimization (LoRA ~60% compute reduction), hallucination control with citation grounding, and enterprise security/governance. Previously at Intuit, delivered an interpretable churn prediction system (PySpark/Databricks, Airflow/Azure ML) that improved retention targeting ~12%.”

“Backend engineer with hands-on experience designing and refactoring scalable Node.js/MongoDB systems and building Python/FastAPI services. Emphasizes production-grade security (JWT, refresh tokens, RBAC, Supabase Auth, RLS) and reliability practices like strong testing, monitoring, and rollback planning, including resolving concurrency and token/validation edge cases.”

“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.”

“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.”