Vetted Apache Airflow Professionals

“LLM engineer who has deployed a production system combining LLMs with causal inference (DoWhy) to enable counterfactual “what-if” analysis for experimental research, including a robust variable-mapping/validation layer to reduce hallucinations. Also partnered with non-technical operations leadership at Irriion Technologies to deliver an AI-assisted onboarding workflow that cut onboarding time by 50% and reduced manual errors by ~40%.”

“AI/LLM engineer with production experience building an LLM-powered investment recommendation system using RAG and chatbots, deployed via Docker/CI/CD and scaled on Kubernetes. Demonstrated measurable performance wins (sub-200ms latency) through QLoRA fine-tuning and TensorRT INT8/INT4 quantization, plus strong MLOps/orchestration background (Airflow ETL + scoring, MLflow monitoring) and stakeholder-facing delivery using demos and Tableau dashboards.”

“Designed and deployed end-to-end LLM-powered AI agent systems to automate knowledge-intensive workflows across marketing/GTM, recruiting, and support. Brings production reliability rigor (evaluation pipelines, monitoring, testing, A/B experiments) plus orchestration expertise (Airflow, Prefect, custom Python) and a track record of translating non-technical stakeholder goals into working AI solutions (e.g., personalized customer engagement agent at Lara Design).”

“Python backend engineer (BetterWorld Technology) who owns microservice systems end-to-end on Azure, including Kubernetes deployments, CI/CD, and production monitoring/alerting. Has hands-on experience integrating SQL/NoSQL (including Cosmos DB with vector search/graph workflow) and has built a Kafka + Spark Streaming pipeline to Snowflake with a reported 40% latency reduction.”

“AI/LLM engineer who built a production resume-parsing and candidate-matching platform at Quadrant Technologies, combining agentic LangChain workflows, VLM-based document template extraction (~85% accuracy), and a hybrid RAG backend for resume-to-JD search. Notably integrated automated LLM evals and metric-based CI/CD quality gates to catch silent prompt/model regressions, and led a 3-person team across frontend/backend/testing.”

“Built a production-style LLM document summarization/generation workflow that mitigates token limits and reduces hallucinations using semantic chunking, FAISS-based embedding retrieval (top-k via cosine similarity), and section-wise generation. Orchestrated the end-to-end pipeline with AWS Step Functions and aligned outputs with sales stakeholders through demos, visuals, and documentation.”

“LLM/AI engineer who built a production automated document-understanding pipeline on Azure using a grounded RAG layer, designed to reduce manual review time for unstructured financial documents. Demonstrates strong real-world scaling and reliability practices (Service Bus queueing, Kubernetes autoscaling, observability, retries/circuit breakers) plus rigorous evaluation (shadow testing, replaying traffic, multilingual edge-case suites) and stakeholder-friendly, evidence-based explainability.”

“LLM/RAG practitioner who has taken a support-ticket triage automation system from prototype to production, building the full pipeline (fine-tuned models, FastAPI inference services, vector storage, monitoring) and delivering measurable impact (~40% reduction in triage time). Demonstrates strong operational troubleshooting of LLM/agentic workflows (observability-driven debugging, fixing agent routing/looping) and supports adoption through tailored demos and sales-aligned technical communication.”

“Applied AI/ML engineer with hands-on production experience building a RAG-based AI assistant for pharmaceutical maintenance troubleshooting using LangChain + FAISS/Pinecone, including a custom normalization layer to handle inconsistent terminology and duplicate document revisions. Also built Airflow-orchestrated pipelines for document ingestion/embeddings and predictive maintenance workflows (SCADA ETL, drift-based retraining), and partnered closely with production supervisors/quality engineers via Power BI dashboards and real-time alerts.”

“Cross-functional operator/chief-of-staff style leader who took a product from prototype to a live pilot in 3 months, spanning public-sector data normalization, an ML matching engine, a secure API, and KPI/investor demo instrumentation. Strong focus on executive alignment and productivity via Notion-based operating systems plus automated reporting (Python/Power BI), with experience supporting fundraising and go-to-market narratives.”

“Backend/full-stack engineer who has owned production TypeScript systems in both fintech-style transaction/rewards flows and HIPAA-regulated healthcare platforms. Deep focus on correctness and reliability (idempotency, retries/DLQs, reconciliation, observability) plus strong infra automation (Docker/Terraform/CI-CD) and measurable performance wins (40% query improvement, 90% test coverage).”

“Built and shipped both a solo real-time multiplayer Spades game (TypeScript monorepo with shared client/server engine) and a production internal LLM-powered document Q&A tool for a SaaS company. Demonstrates strong RAG pipeline design (Pinecone + embeddings + reranking), rigorous eval/regression practices, and pragmatic data ingestion/observability work across Confluence, Notion, and messy PDFs/OCR—backed by clear metric improvements (P@1 61%→78%, escalations 40%→22%).”

“Built and deployed a production LLM-powered internal AI assistant using a RAG pipeline to help teams search internal PDFs/knowledge bases and generate grounded summaries/answers. Demonstrates strong end-to-end ownership (ingestion through APIs) plus production rigor (monitoring/logging/CI-CD, evaluation metrics) and practical optimizations for hallucination, latency, and answer quality (thresholding, fallbacks, caching, async, re-ranking, two-tier model routing).”

“Built and shipped a production real-time content moderation platform for Zoom/WebEx-style meetings, combining Whisper speech-to-text with fast NLP classifiers and REST APIs to flag hate speech, bias, and HIPAA-related content under strict latency constraints. Demonstrates strong MLOps/infra depth (Airflow, Kubernetes, Terraform/Helm, observability) and a pragmatic approach to reducing false positives via threshold tuning, context validation, and hard-negative data—while partnering closely with compliance and product stakeholders.”

“Built and productionized an LLM+RAG lending assistant on AWS to help loan officers quickly answer questions from credit policies and prior decisions, tackling hallucinations with retrieval-only responses and a no-context fallback. Also automated end-to-end ETL and model retraining/deployment using Apache Airflow, and has experience translating clinical stakeholder needs (doctors/care managers) into ML features, metrics, and dashboards.”

“Developed and productionized VL-Mate, a vision-language, LLM-powered assistant aimed at helping visually impaired users understand their surroundings and query internal knowledge. Emphasizes reliability and safety via confidence thresholds, uncertainty-aware fallbacks, hallucination grounding checks, and rigorous offline + user-in-the-loop evaluation, with experience orchestrating multi-step LLM pipelines (LangChain-style and custom Python async) and deploying on containerized infrastructure.”

“Built and deployed a production LLM/RAG intelligent document understanding platform for healthcare clinical documents (notes, discharge summaries, diagnostic reports), integrating spaCy entity extraction, Pinecone vector search, and a Spring Boot API on AWS with monitoring and guardrails. Demonstrates strong MLOps/orchestration (LangChain, Airflow, Kubeflow/Kubernetes) and a metrics-driven evaluation approach, and partnered with a healthcare operations manager to cut manual review time by 80%.”

“Healthcare-focused ML/LLM engineer who built a production hybrid RAG workflow to automate prior authorization by retrieving from medical guidelines/historical cases (FAISS) and generating grounded rationales for clinicians. Strong in operationalizing ML with Airflow/Kubeflow/MLflow on SageMaker, optimizing latency (ONNX/quantization/async), and reducing hallucinations via evidence-only prompting; also partnered closely with clinical ops to deploy a readmission prediction tool used in daily rounds.”

“Built a production lead-generation system using AI agents that researches the internet for relevant leads and integrates RAG-based contact enrichment/shortlisting aligned to existing CRM data, enabling sales reps to focus more on selling. Also has hands-on AWS data orchestration experience (Glue, Step Functions) moving raw data into Redshift and evaluates agent performance with human-in-the-loop plus BLEU/perplexity metrics.”

“Data/ML engineer from HCLTech who modernized enterprise data by linking fragmented financial and supply-chain data across SAP/SQL Server/Snowflake using NLP entity linking and embeddings (FAISS). Delivered measurable impact including ~40% reduction in manual error-log triage and entity-linking accuracy improvements from ~86% to ~93%, with results surfaced in Power BI for real-time analytics.”

“Full-stack/AI engineer who has shipped production systems spanning real-time analytics dashboards and an internal LLM-powered knowledge assistant. Experienced with RAG pipelines (embeddings/vector DB, semantic retrieval, query rewriting) plus evaluation loops and guardrails, and builds observable Kafka-based data pipelines monitored with Prometheus/Grafana.”

“Backend engineer experienced in evolving high-scale legacy on-prem systems into cloud-native, event-driven microservices on AWS/Kubernetes (noted peak traffic ~1.5M QPS). Strong focus on reliability engineering and operational excellence—SLO-driven observability, GitOps/canary rollouts, chaos testing, and preventing cascading failures (e.g., retry-storm mitigation).”

“Backend engineer with legal-tech and AI workflow experience: built JurisAI, an end-to-end legal research system using OCR + embeddings + Pinecone vector search to deliver citation-grounded LLM answers with safe failure modes (~90% recall@K). Also led a GW Law metadata migration into Caspio with batch validation and parallel rollout, and has strong FastAPI/GCP production reliability and observability practices.”

“Built and deployed production NLP/LLM systems including a multilingual (5-language) health misinformation detection pipeline with latency optimization (batching/quantization/caching) and explainability (gradient-based attention visualizations). Experienced orchestrating end-to-end AI workflows with Airflow and Prefect, and partnering with customer support ops to deliver an AI agent for ticket summarization and priority classification with clear, measurable acceptance criteria.”