Vetted MLflow Professionals

Mark Wlodawski

Screened

Senior Unity Developer specializing in AI/LLM systems and multiplayer VR

Orlando, FL11y exp

“Backend/data engineer focused on AWS-native Python systems: built a FastAPI microservice on ECS/Fargate serving real-time analytics at millions of daily requests with strong reliability (OAuth2/JWT, retries/timeouts, correlation IDs) and autoscaling. Also delivered Glue/PySpark ETL pipelines to curated S3 Parquet/Athena with schema evolution + data quality controls, owned Airflow pipeline incidents, and has a track record of measurable performance and cost optimizations (e.g., ~80%+ query latency reduction; reduced logging/NAT/Fargate spend).”

“Built a production RAG chatbot for Worcester Polytechnic Institute that indexes 500+ webpages using FAISS + Llama 3, with strong grounding/hallucination controls (confidence thresholds and citations). Also has internship experience orchestrating multi-step ETL pipelines with AWS Step Functions and delivered a 30x faster fraud/claims triage workflow at Munich Re using association rules and stakeholder-friendly dashboards.”

“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”

“Candidate has hands-on experience at Cognizant building production-grade automation and integration solutions across Python ML services, Java microservices, Kafka, and Selenium-based UI testing. They stand out for a strong reliability mindset—covering failure modes, observability, flaky test hardening, and translating ambiguous payment-system business processes into resilient end-to-end automated workflows.”

“AI/ML engineer who led Impacter AI’s production deployment of a specialized outreach LLM (CharmedLLM) fine-tuned on GPT-4.1, cutting API costs ~40% while boosting outreach effectiveness ~60%. Built the supporting MLOps and data infrastructure (MLflow, Kubernetes, PySpark, Kafka) and has agentic AI experience from University of Dayton, using LangChain + RAG and vector search (Pinecone) to improve reliability and reduce hallucinations.”

“GenAI/LLM engineer with production experience at Allstate building an end-to-end document intelligence workflow for insurance operations—automating document intake, classification, and risk signal extraction. Emphasizes high-reliability design for regulated/high-stakes outputs using schema enforcement, confidence thresholds, validation rules, and human-in-the-loop routing, with metric-driven offline evaluation and production monitoring.”

Mohammed Saalim Kartapillai

Screened

Senior Backend/Full-Stack Engineer specializing in data platforms and cloud microservices

Chicago, Illinois7y exp

“Backend engineer who built and shipped an end-to-end AI outreach product (LazyMails) combining a LinkedIn-scraping Chrome extension with a FastAPI/Postgres backend and Gemini-powered email generation, achieving major personal productivity gains. Also has enterprise experience at TCS on Humana’s 500k+ user wellness platform running Kubernetes microservices with Azure DevOps CI/CD, plus Kafka-based real-time eligibility event streaming and GitOps-driven operations.”

“Built and deployed a production NLP sentiment analysis system at Piper Sandler to turn noisy, finance-specific customer feedback into scalable insights. Demonstrates strong end-to-end MLOps: fine-tuning BERT, improving label quality, monitoring for language drift, and automating retraining/deployment with Airflow and Docker (plus Kubeflow exposure).”

“ML/NLP engineer with Industrial IoT experience who built an end-to-end anomaly detection and GenAI explanation system: AWS (S3, PySpark, EC2/Lambda) pipelines feeding dashboards, plus transformer-embedding vector search to connect anomalies to noisy maintenance notes and past events. Demonstrated measurable impact (15% lift in defect detection; ~35% reduction in manual review; 35% fewer preprocessing errors) and strong productionization practices (orchestration, monitoring, rollback, data-quality controls).”

“Robotics software engineer focused on backend/integration for indoor autonomous mobile robots, with hands-on ROS 2 experience integrating Nav2/AMCL/TF2 and LiDAR/camera pipelines. Emphasizes production readiness—robust failure recovery, QoS-tuned distributed communication, and strong observability (logging/health checks)—validated through Gazebo simulation, sensor-data replay debugging, and Docker-based CI/CD deployment.”