Vetted LightGBM Professionals

“AI/ML practitioner with Northern Trust experience who has shipped production LLM systems (internal support assistant) using RAG, vector databases, orchestration (LangChain/custom pipelines), and rigorous monitoring/feedback loops. Also built AI-driven fraud detection/risk monitoring solutions in a regulated financial environment, emphasizing explainability (SHAP), audit readiness, and stakeholder trust through dashboards and clear communication.”

“At JP Morgan Chase, built and deployed a production LLM-powered RAG knowledge assistant to help fraud investigators and risk analysts quickly navigate regulatory updates and internal policies, reducing investigation delays and compliance risk. Strong focus on secure retrieval (RBAC filtering), reliability (layered testing + observability), and production constraints (latency/SLOs), with Airflow-orchestrated, auditable ML pipelines.”

“Built and shipped a production LLM-powered personalized client engagement assistant in the financial domain, balancing real-time recommendations with strict privacy/compliance requirements. Demonstrates strong MLOps/LLMOps depth (Airflow + MLflow, containerized microservices, drift monitoring) and a privacy-by-design approach validated in collaboration with risk and compliance teams.”

“Built and deployed an LLM-powered analytics assistant enabling business users to ask questions in plain English and receive validated Spark SQL executed in Databricks, with a Streamlit/Flask UI. Addressed strict client schema-privacy constraints by implementing a RAG strategy and ultimately leveraging AWS Bedrock and fine-tuned reference docs. Also has production ML pipeline experience using Docker + Airflow and AWS (S3/ECS/EC2) for financial classification models.”

“AI/ML engineer with HP experience building and productionizing an LLM-powered document intelligence platform (LangChain + Pinecone) to deliver semantic search and contextual Q&A across millions of enterprise support documents. Demonstrates strong MLOps and scaling expertise (Airflow, Kubernetes autoscaling, Triton GPU inference, monitoring with Prometheus/W&B) plus a structured approach to evaluation (A/B tests, shadow deployments, failover) and effective collaboration with non-technical stakeholders.”

“GenAI/data engineering practitioner with production experience across Equinix, Optum, and Citibank—built an Azure OpenAI (GPT-4) + LangChain document intelligence platform processing 1.5M+ docs/month and a HIPAA-compliant Airflow healthcare pipeline handling 5M+ claims/day. Also delivered a real-time fraud detection + explainability system using LightGBM and a fine-tuned T5 NLG component, improving fraud accuracy by 15%+ while partnering closely with compliance stakeholders.”

“Machine learning/NLP engineer who built a production-oriented retrieval-based AI system at Morgan Stanley for healthcare use cases, combining RAG over unstructured patient records with deep-learning medical image segmentation (U-Net/Mask R-CNN). Strong in end-to-end pipelines and MLOps (Spark/MongoDB, AWS SageMaker, CI/CD, monitoring, automated retraining) and in entity resolution/data quality validation for noisy clinical data.”

“ServiceNow engineer who built and launched a production LLM-powered ticket resolution/knowledge assistant using RAG (LangChain + Hugging Face embeddings + vector search) integrated into internal support dashboards via REST APIs. Optimized the system from ~6–8s to ~2–3s latency while improving usability with concise, cited answers and guardrails (grounding + similarity thresholds), delivering ~30–35% reduction in manual ticket investigation effort.”

Pooja Dokuri

Screened

Mid-level AI/ML Engineer specializing in GenAI, RAG pipelines, and cloud MLOps

Remote, USA4y exp

“Built and deployed a production LLM + vector search clinical decision support system at UnitedHealth Group, retrieving medical evidence and patient context in real time for prior authorization and risk scoring. Strong in end-to-end RAG architecture (Hugging Face embeddings, Pinecone/FAISS, SageMaker, Redis) plus orchestration (Airflow/Kubeflow) and rigorous evaluation/monitoring, with demonstrated ability to align solutions with clinical operations stakeholders.”

Prasanna Chelliboyina

Screened

Mid-level Machine Learning Engineer specializing in forecasting, NLP, and GenAI

United States6y exp

“GenAI/ML engineer with production experience building multilingual LLM systems (English/Spanish) and RAG-based clinical documentation summarization at Walgreens, combining prompt engineering, structured output validation, and rigorous evaluation (ROUGE + pharmacist review). Also orchestrated end-to-end ML pipelines for demand forecasting using Apache Airflow, PySpark, and MLflow with scheduled retraining and production monitoring.”

Sai Charan Kolla

Screened

Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps on AWS

TX, USA5y exp

“LLM engineer who built a production document intelligence/RAG pipeline to extract structured data from thousands of unstructured PDFs, cutting manual review time by 60%. Experienced with LangChain and Airflow orchestration plus rigorous evaluation (labeled datasets, prompt testing, HITL review, monitoring) to improve accuracy and reduce hallucinations while partnering closely with non-technical operations stakeholders.”

“Built and shipped a production-grade agentic RAG system at CVS Health for patient adherence and medication recommendations, processing 20k+ patient records/day. Strong focus on real-world reliability: hybrid retrieval tuned with re-ranking (<400ms latency), strict JSON/schema validation and tool guardrails, and monitoring/drift detection that reduced MTTD from 6 days to 18 hours while improving recommendation accuracy (+8%) and cutting escalations (~23%).”

Sachin Komati

Screened

Mid-level AI/ML Engineer specializing in GenAI, RAG, and healthcare ML

Florida, USA5y exp

“Built an end-to-end GenAI/RAG platform for financial compliance and research at BlackRock, focused on safe, auditable answers in a highly regulated environment. Combines strong LLM engineering depth with production platform skills and delivered clear business impact, including reducing research/compliance turnaround from hours to seconds, improving retrieval relevance by 22%, and cutting inference costs by 75%.”

“Backend/founding-engineer-style builder who designed and evolved a near-real-time customer churn prediction platform (FastAPI + AWS SageMaker/Lambda + Redis + MLflow) to enable real-time retention actions, reporting ~18% churn reduction. Demonstrates strong production engineering in secure API design, incremental migrations with data integrity safeguards, and robustness improvements in async pipelines (idempotency, DLQs, retry visibility).”

“GenAI/ML engineer from Lucid Motors who built and productionized an LLM-powered RAG diagnostic assistant for manufacturing and maintenance teams, deployed on AWS with Docker/Kubernetes and MLflow. Demonstrates end-to-end ownership from retrieval/prompt design to scalability, monitoring, and workflow integration via APIs, plus production ML pipeline orchestration with Kubeflow (Spark/Kafka + TensorFlow) for predictive maintenance use cases.”

“Built and deployed a production LLM-powered RAG assistant for semiconductor manufacturing failure analysis, reducing engineer triage effort by grounding outputs in retrieved evidence and gating responses with SPC + ML signals (LSTM anomaly scores, XGBoost probabilities). Experienced with LangChain/LangGraph to ship reliable, observable multi-step agents with branching/fallback logic, and evaluates impact using both technical metrics and business KPIs like mean time to triage and downtime reduction.”

“AI/ML engineer with production experience building LLM-powered document intelligence and customer support systems in healthcare/insurance, emphasizing high-accuracy RAG, long-document processing, and robust monitoring/fallback mechanisms. Also automates and scales ML lifecycle workflows using Apache Airflow and Kubeflow, and partners closely with non-technical operations stakeholders to drive adoption.”

“Red Hat ML/LLM engineer who designed and deployed a production LLM-powered customer support automation system using RAG, improving latency by 30% via PEFT and vector search optimization. Built security and governance into retrieval (access-level filtering, encrypted Pinecone/ChromaDB) and delivered SHAP-based explainability via a dashboard for non-technical stakeholders. Experienced orchestrating distributed ML/RAG pipelines across AWS SageMaker and OpenShift with Airflow/Prefect, plus multi-agent workflows using CrewAI and LangGraph.”

“Data/ML practitioner with hands-on experience building NLP systems from prototype to production: delivered a Twitter sentiment classifier with robust preprocessing, SVM modeling, and Power BI reporting, and built entity-resolution pipelines for messy multi-source customer data (reporting ~95% improvement in unique entity identification). Also implemented semantic linking/search using SBERT embeddings with FAISS vector retrieval and domain fine-tuning (reported ~15% precision lift), and applies production workflow best practices (Airflow/Prefect, Docker, Azure ML/Databricks, Great Expectations).”

“Built and deployed a production LLM-powered RAG document intelligence/Q&A system for healthcare prior authorization, reducing manual medical document review time and improving decision efficiency. Strong in end-to-end LLM application engineering (LangChain/LangGraph), retrieval quality improvements (hybrid search, embedding tuning, chunking strategies), and rigorous evaluation/monitoring for reliability.”

Harrishkumar Loganathan

Screened

Mid AI/Machine Learning Engineer specializing in FinTech and Generative AI

Remote, USA3y exp

“AI/ML engineer with hands-on ownership of enterprise LLM deployments at Freshworks, including a large-scale RAG chatbot serving 15,000+ users across six departments. Stands out for combining deep production engineering skills—AWS microservices, Kubernetes, observability, retrieval quality, and faithfulness evaluation—with strong cross-functional stakeholder leadership and prior large-scale fraud data pipeline experience at Socure.”

“ML/AI engineer with hands-on ownership of production GenAI and computer vision systems, spanning experimentation, deployment, monitoring, and iterative optimization. Stands out for shipping an enterprise RAG platform that cut manual review by 50% and a defect detection pipeline that reduced report generation from 15 minutes to under 1 second while maintaining high uptime and strong operational discipline.”

“Built and deployed a production LLM-powered internal documentation assistant using embeddings, a vector database, and a RAG pipeline to reduce time spent searching PDFs/manuals. Experienced in orchestrating end-to-end LLM workflows with Airflow/LangChain, improving reliability via monitoring/error handling, and driving measurable quality through retrieval and hallucination-focused evaluation metrics.”

“McKinsey data/ML practitioner who led production deployment of an entity resolution + semantic search platform for unstructured finance and healthcare data, integrating with legacy systems under HIPAA constraints. Deep hands-on stack across transformers (spaCy/HF BERT), embeddings + FAISS, and production MLOps/workflow tooling (Airflow, Docker, CI/CD, Prometheus/Grafana), with reported gains of +30% decision speed and +25% search relevance.”