Vetted Regression Analysis Professionals

“Backend/data engineer in healthcare who built an AWS-based clinical analytics platform from scratch (DynamoDB/S3/Airflow/dbt) with sub-second clinician query goals, 99.9% uptime, and HIPAA-grade controls (KMS encryption, IAM RBAC, audit trails). Also modernized ML delivery by replacing a manual 4-hour deployment with a 30-minute Docker/GitHub Actions CI/CD pipeline using parallel runs, parity testing, and rollback, and caught critical EHR data edge cases (date formats/timezones) that could have impacted patient care.”

“Robotics/embodied-AI practitioner who built a TurtleBot3 LiDAR-fingerprint localization pipeline end-to-end (autonomous data collection + multi-head NN) achieving ~30 cm error in a 10x10 m space. Also has industry experience at Infineon building large-scale production data/AI pipelines and rapidly fixing a deployed recommendation system by correcting upstream data normalization, improving accuracy by 20%+.”

“LLM/agent engineer who built a production multi-agent research automation system using LangGraph (planner, retriever with FAISS, supervisor, evaluator) with structured outputs and citation tracking for traceable reports. Emphasizes reliability and operations—LangSmith-based observability, multi-level testing, hallucination mitigation, and latency/cost controls—plus prior experience as a Computer Vision Software Engineer at Deepsight AI Labs working directly with non-technical customers.”

“Graduate Assistant Intern at Friends University who built and deployed a GenAI-driven requirement understanding system that automates extraction and semantic grouping of technical requirements from large unstructured documents. Demonstrates strong LLM engineering rigor (golden datasets, regression testing, post-processing validation) and production-minded delivery using LangChain/LlamaIndex orchestration, FastAPI microservices, Docker, and cloud deployment.”

“ML/NLP engineer with hands-on experience building production systems for unstructured insurance claims and customer data linking. Delivered measurable impact at scale (millions of documents), combining transformer-based NLP, vector search (FAISS/Pinecone), and human-in-the-loop validation, and has strong production workflow/observability practices (Airflow, AWS Batch, Grafana/Prometheus).”

“ML/NLP engineer (Beyond Identity) who built production semantic search and entity-resolution systems over internal security documentation, using LDA + BERT embeddings with FAISS/Pinecone to cut search time by 30%. Also scaled a real-time anomaly detection pipeline to millions of events/day with Spark and AWS Lambda, with strong emphasis on measurable validation (Precision@k, MRR, F1, ARI).”

“Backend engineer who built and evolved a gun-parts price tracking platform focused on accurate historical pricing and fast graph-ready APIs. Experienced migrating an Express backend to NestJS incrementally with parallel routing, feature flags, and careful data integrity controls, and has a security-focused approach to API design (JWT/OAuth, RBAC, row-level access via scoped queries).”

“GenAI/LLM engineer who built and deployed a production RAG system for enterprise document search and decision support, cutting manual lookup time by 40%+. Experienced with LangChain/LangGraph agent orchestration plus Airflow/Prefect for ingestion and incremental reindexing, with a strong focus on reliability (testing, observability) and stakeholder-driven metrics.”

“LLM/agent builder who shipped a live consumer AI-agent app (kalpa.chat) that visualizes complex reasoning as interactive graphs and abstracts multi-provider model usage via a unified wallet. Professionally has applied LangChain/LangGraph to IVR parsing and to scaling a football video-generation pipeline at DAZN, including shipping a VAR-specific retrieval/order fix via SQL after iterating with a non-technical PM.”

“ML/LLM engineer with Meta experience building production AI systems for near real-time user-report classification and summarization under strict latency (<250ms), safety, cost, and privacy constraints. Has hands-on MLOps/orchestration experience (Airflow, Spark, MLflow, Kubernetes, Docker, GitHub Actions) plus observability (Prometheus/Grafana) and applies rigorous evaluation, staged rollouts, and A/B testing to keep agent workflows reliable in production.”

“Built and deployed LLM-powered agentic chatbot and text-to-SQL systems using LangGraph/LangChain (and Bedrock), structuring workflows as DAGs with planning/replanning and validation to improve tool-calling reliability and reduce hallucinations. Operates production feedback loops with online/offline metrics, drift detection, and LangSmith-based evaluation pipelines, and regularly partners with business stakeholders and clinicians using slide decks and visual charts.”

“ML/LLM engineer with production experience building a compliant RAG-based virtual assistant at Intuit, optimizing embeddings and FAISS retrieval (including PCA) for low-latency, privacy-controlled search and deploying via AWS SageMaker containers. Also built scalable Airflow+MLflow pipelines using Docker and KubernetesExecutor, cutting training cycles by 37%, and partnered with civil engineers/project managers at Aegis Infra to deliver predictive maintenance for construction equipment.”

“Backend/data engineer who builds Python (FastAPI) data-processing API services for internal analytics/reporting, emphasizing modular architecture, async performance tuning, and reliability patterns (health checks, retries, observability). Also migrated legacy on-prem ETL pipelines to Azure using ADF/Data Lake/Functions and implemented a near-real-time ingestion flow with Event Hubs plus watermarking to handle late events and deduplication.”

“Backend engineer who has owned end-to-end delivery of Python/FastAPI microservices for real-time data processing and alerting, including performance tuning (Postgres optimization, caching, async processing). Strong DevOps/GitOps background: Docker + Kubernetes deployments with GitHub Actions CI/CD and ArgoCD-driven GitOps, plus experience supporting phased on-prem to AWS migrations and building Kafka-based streaming pipelines.”

“LLM engineer/data engineer who has deployed production RAG systems for internal-document Q&A, building end-to-end ingestion, embedding, vector search, and FastAPI serving while actively reducing hallucinations and latency through rigorous retrieval tuning and caching. Also experienced in orchestrating cloud data pipelines (Airflow, AWS Glue, Azure Data Factory) and partnering with non-technical business teams to deliver AI solutions like automated document review.”

“Embedded/software engineer with hands-on Raspberry Pi work building a WhatsApp-controlled camera/servo system using TCP/IP plus Selenium automation of WhatsApp Web. Brings production DevOps experience from Infosys (Docker/Kubernetes, CI/CD, microservices, Kafka) and a methodical hardware/software debugging workflow using lab tools like oscilloscopes and multimeters.”

“AI/NLP-focused practitioner who built a zero-/few-shot LLM event extraction system on the long-tail Maven dataset, combining prompt-structured outputs with LoRA/QLoRA fine-tuning and rigorous F1 evaluation. Also implemented entity resolution/data cleaning pipelines and embedding-based semantic search using Sentence-BERT + FAISS, and has healthcare experience delivering a multilingual speech/translation mobile prototype using HIPAA-compliant Azure Cognitive Services.”

“ML/LLM engineer with production experience at General Motors building Transformer-based search and recommendation personalization for a high-traffic vehicle platform. Delivered significant KPI gains (17% conversion lift, 14% bounce-rate reduction) and optimized real-time inference via ONNX Runtime and INT8 quantization while implementing robust MLOps (Airflow/MLflow, monitoring, drift-triggered retraining) and stakeholder-facing explainability/dashboards.”

“AI/ML practitioner in healthcare/insurance (Blue Cross Blue Shield) who built and deployed a production NLP system to classify patient risk from unstructured clinical notes. Experienced in end-to-end pipeline orchestration (Airflow, AWS Step Functions/Lambda/SageMaker) and real-time optimization (BERT to DistilBERT on AWS GPUs), with strong clinician collaboration to drive adoption.”

“Deployed a real-time LLM-driven call center summarization and agent-assist platform at Fifth Third Bank, combining transformer models (BERT/GPT) with FastAPI inference on AKS and vector storage (ChromaDB/PostgreSQL). Emphasizes production-grade reliability (autoscaling, CI/CD, monitoring) and measurable evaluation (A/B testing), and translates model outputs into business-facing Power BI insights for call center leadership.”

“Product-focused full-stack engineer (Spring Boot/Django + React/TypeScript) with deep experience building multi-tenant, enterprise workflow and supply-chain/order-tracking systems. Owned an end-to-end Workflow SLA Breach Prediction & Alerting feature integrating Azure ML for a cloud workflow platform used by ~10,000 enterprise users, and has hands-on AWS operations experience resolving real production latency/scaling incidents via query optimization and Redis caching.”

“Healthcare AI/ML engineer with Epic experience who built and deployed a HIPAA-compliant GPT-4 RAG clinical assistant over large medical document sets, emphasizing privacy controls and low-latency performance. Also automated end-to-end retraining and deployment of patient risk models using orchestration/CI-CD (Jenkins, SageMaker, MLflow), cutting deployment time from hours to minutes while improving reliability.”

“Built and deployed a production multimodal language translation platform (text-to-text, speech-to-text, text-to-speech) using fine-tuned pretrained models (NLLB, XLSR), MLflow-orchestrated pipelines, and Docker/Kubernetes on AWS. Worked closely with non-technical linguists to tackle data cleaning and dialect variation in minority languages, improving accuracy through consistent evaluation and monitoring.”

“ML/NLP engineer with healthcare and industrial IoT experience: built an Optum pipeline that converted 2M+ physician notes into structured entities and linked them with claims/pharmacy data to create an actionable patient timeline. Deep hands-on expertise in production NER, entity resolution, and hybrid search (Elasticsearch + embeddings/FAISS), plus robust data engineering practices (Airflow, Spark, data contracts, auditability) and experimentation-to-production rollout via shadow mode and feature flags.”