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Vetted XGBoost Professionals
Pre-screened and vetted.
Bhavana Anna
Mid-level AI/ML Engineer specializing in fraud detection and Generative AI (RAG)
“AI/ML engineer who has shipped production LLM and ML systems, including a RAG pipeline that ingested ~500k insurance/client documents to help adjusters answer questions faster and more consistently. Experienced in handling messy real-world document formats, tuning retrieval/chunking, and reducing latency via vector search optimization, precomputed embeddings, and caching. Also built orchestrated fraud-detection deployment workflows using AWS Step Functions and SageMaker, and partners closely with non-technical operations teams on NLP automation.”
sai Pavan
Mid-level AI/ML Engineer specializing in MLOps, NLP, and real-time ML pipelines
“Built a production, real-time insurance claims document-understanding and fraud-detection pipeline using TensorFlow + fine-tuned BERT, deployed on AWS (SageMaker/Lambda/API Gateway) with automated retraining via MLflow and Jenkins. Addressed noisy documents and latency using augmentation and model distillation (3x faster), cutting claims ops manual review by ~50% and reducing fraudulent payouts.”
Teja Babu Mandaloju
Mid-level Data Scientist/MLOps Engineer specializing in NLP, GenAI, and cloud ML platforms
“AI/ML engineer who led production deployment of a multimodal (text/video/image) RAG system on GCP using Gemini 2.5 + Vertex AI Vector Search, scaling to 10M+ documents with sub-second latency and +40% retrieval accuracy. Strong MLOps/orchestration background (Kubernetes, CI/CD, Airflow, MLflow) with proven impact on reliability (75% fewer incidents) and deployment speed (92% faster), plus experience delivering explainable ML (XGBoost + SHAP + Tableau) to non-technical retail stakeholders.”
Nicholas Kim
Entry-Level Software Engineer specializing in full-stack and backend development
“Full-stack developer who built a workout tracker feature end-to-end on the PERN stack (Postgres/Express/React), including relational schema design, REST APIs, and optimistic UI updates. Also has Next.js App Router experience (dynamic routes, SSG/React Server Components) and a strong quality mindset from Boston Scientific, where they used TDD to support clinically sound ECG analysis software and drove backend test coverage to 97%.”
Sai Bandaru
Mid-level Machine Learning Engineer specializing in fraud detection and LLM systems
“At FiVerity, built and deployed a production LLM/RAG-based Information Gathering Tool for credit union fraud analysts that generates auditable investigation summaries from verified evidence. Focused on high-stakes constraints—hallucination prevention, cross-entity leakage controls, compliance/PII-safe monitoring, and latency—while also shipping customer-facing agentic workflows using CrewAI and LangGraph in close partnership with fraud and compliance stakeholders.”
Anvith Reddy T
Mid-level AI/ML Engineer specializing in Generative AI and MLOps
“ML/AI engineer with hands-on ownership of fraud detection and investigator-assist systems, combining anomaly detection with RAG-based LLM summarization in production. Stands out for translating research ideas into reliable cloud-deployed workflows that improved precision to 92%, cut review time by 25-30%, and increased investigator throughput by roughly 30% while also building reusable Python infrastructure for team-wide velocity.”
Pranathi Nallala
Mid-level AI Engineer specializing in distributed systems and LLM applications
“Built production AI agents that convert natural-language requests into structured workflows using LangChain, tool calling, and a Kafka/Kubernetes backend, with strong emphasis on tracing, validation, and self-correcting failure handling. Also drove a zero-to-one Research Day judging platform spanning React, Flask, RAG, and ILP-based assignment optimization for ~100 live posters, achieving 99% uptime and winning Best Web App.”
Kevin Delong
Senior AI/ML Engineer specializing in Generative AI, LLMs, and RAG systems
“AI/ML engineer with hands-on experience shipping production systems across fintech, travel, and legal use cases. They’ve built end-to-end chatbot, generative content, and RAG solutions on AWS with CI/CD, monitoring, and guardrails, including a loan application platform that generated $3,000 in sales in its first month.”
TingYu Chou
Entry-level ML Systems Engineer specializing in LLM infrastructure and recommender systems
“Engineer with a mature, agent-oriented approach to AI-driven software development, using structured planning, TDD, and verification loops rather than ad hoc prompting. Has hands-on experience acting as a tech lead for multiple AI agents in an LLM intelligent routing project, coordinating implementation, testing, debugging, and edge-case review with strong attention to system tradeoffs.”
Sri vardhini
Junior Software Engineer specializing in AI/LLM full-stack systems
“AI/full-stack engineer who has built zero-to-one internal products around LLMs, RAG, and NLP pipelines, including a conversational data interface and a production AI agent system. Stands out for combining frontend UX for non-technical users with backend/cloud architecture and measurable impact, including a reported 60% reduction in data retrieval time.”
Meghavardhan Ketireddi
Mid-level AI & Machine Learning Engineer specializing in Generative AI and MLOps
“Built a production GPT-4/LangChain/Pinecone RAG “AI Copilot” at Northern Trust to automate financial report generation and analyst Q&A over internal structured (SQL warehouse) and unstructured policy data. Focused on real-world production challenges—grounding and latency—achieving major speed gains (seconds to milliseconds) via MiniLM embedding optimization and Redis caching, and implemented rigorous testing/evaluation with MLflow-backed metrics while aligning compliance and finance stakeholders for deployment.”
Chaitanya Kalagara
Mid-level Machine Learning Engineer specializing in LLMs, GenAI, and Computer Vision
“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.”
KHUSHBU KAKDIYA
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and cloud MLOps
“Built and deployed a production LLM/RAG system at CVS to automate clinical documents, addressing PHI compliance, retrieval accuracy, and latency; achieved a 35–40% reduction in review effort through chunking and FP16/INT8 optimization. Also has experience translating AI outputs into actionable insights for non-technical stakeholders (sports analysts).”
Nitin Shivakumar
Senior Data Scientist specializing in healthcare ML, LLMs, and responsible AI
“Clinical data scientist who has built an agentic LLM-powered literature review assistant (with RAG-style storage/retrieval) to identify predictors for downstream predictive modeling. Also delivered a patient-focused progression analysis model using Databricks + Airflow orchestration, partnering closely with clinicians to define targets and validate that model insights aligned with clinical expectations.”
HemaSri Perumalla
Mid-level AI/ML Engineer specializing in fraud detection and healthcare predictive analytics
“ML/AI engineer with production experience in high-scale banking fraud detection at Truist, building an end-to-end pipeline (Airflow/AWS Glue/Snowflake, PyTorch/sklearn) with automated retraining and Kubernetes-based deployment; delivered measurable gains (22% fewer false positives, 15% higher recall) and reduced manual ops ~40%. Also partnered with clinicians at Kellton to deploy an LLM system for summarizing/classifying clinical notes, improving review time and decision speed.”
Shruti Rawat
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps for financial services
“Built and deployed a production Llama 3-based RAG document Q&A system using FAISS, addressing context-window limits through chunking and keeping retrieval accurate by regularly refreshing embeddings. Has hands-on orchestration experience with LangChain and LlamaIndex for multi-step LLM workflows (including memory management) and collaborates with non-technical teams (e.g., marketing) to deliver AI solutions like recommendation systems.”
Ramya Konda
Mid-level AI/ML Engineer specializing in healthcare ML and generative AI
“AI/LLM engineer at Humana who built and deployed a HIPAA-aware RAG system for clinical record retrieval, cutting search time dramatically and improving retrieval efficiency by 30%. Experienced with Spark-scale data preprocessing, QLoRA fine-tuning, LangChain orchestration, and MLflow+SageMaker integration, with a strong testing/evaluation discipline (A/B tests, human eval) to hit 95%+ accuracy and production latency targets.”
“AI/ML engineer with banking domain experience (M&T Bank) who built a production credit-risk prediction and reporting platform combining ML models (XGBoost/TensorFlow) with a RAG pipeline (LangChain + GPT-4) over compliance documents. Delivered measurable impact (≈20% better risk detection/precision, 50% less manual reporting) and productionized workflows on Vertex AI/Kubeflow with CI/CD and monitoring; also implemented embedding-based semantic search using FAISS/Pinecone.”
Bhavya Sri Gunnapaneni
Mid-level AI/ML Engineer specializing in fraud detection and NLP
“Built production AI/RAG-style systems for message Q&A and insurance claims workflows, combining data ingestion, indexing/retrieval, and LLM integration with fallback modes. Has hands-on orchestration experience (Airflow, Prefect, LangChain) and cites large operational gains (claims processing reduced to ~45 seconds; manual review -50%; false alerts -30%) through automated, monitored pipelines and close collaboration with non-technical stakeholders.”
Swati Swati
Senior Data Scientist/Software Engineer specializing in ML systems and cloud DevOps
“AI software engineer with experience spanning LLM/RAG production systems and regulated fintech infrastructure. Built an end-to-end natural-language-to-SQL analytics assistant (Weaviate + GPT-4 + Supabase) shipped as an API with 92% accuracy and major time savings for non-technical users, and also owned demand-forecasting and CI/CD/containerization improvements for a Bank of America core banking deployment at Infosys.”
Atharva Deshmukh
Mid-level AI/ML Engineer specializing in GenAI and cloud MLOps
“Applied LLMs to high-stakes domains (wildfire risk for emergency teams and loan approval via a fine-tuned IBM Granite model), with a strong focus on reliability—using RAG-based cross-validation to reduce hallucinations and continuous ingestion pipelines (MODIS satellite imagery via AWS Lambda) to keep data current. Experienced in production orchestration and MLOps-style workflows using Airflow, AWS Step Functions, and SageMaker Pipelines, and collaborates closely with analysts on KPI-driven evaluation.”
Srichandan Kota
Senior Full-Stack AI Engineer specializing in Generative AI and FinTech
“Backend engineer who built and owned an AI-powered financial research product end-to-end, using a typed NestJS/GraphQL backend with LangGraph-style agent routing to produce sourced, structured financial analysis. Emphasizes finance-grade correctness (Zod validation, metric registries, unit/empty-result guardrails) while keeping latency low via batching, caching, and fast token streaming, and has led incremental migrations using strangler/feature-flag/shadow traffic patterns.”
Veena Vyshnavi Garre
Senior Full-Stack Software Engineer specializing in cloud-native systems and AI/ML
“Backend engineer who significantly evolved an internal Resource Manager platform, moving from a monolith to microservices and improving onboarding speed while reducing integration errors. Has hands-on experience building reliable and secure Python/FastAPI APIs (Pydantic schemas, circuit breakers, caching, metrics/alerts) and leading zero-downtime migrations with strong data integrity patterns (dual writes, idempotency, reconciliation checks).”
Sai Krishna Mallikanti
Mid-level AI & Data Scientist specializing in LLMs, RAG, and healthcare NLP
“Built a production LLM/RAG solution for healthcare operations teams to query large policy and care-guideline repositories in natural language. Improved domain alignment using vector retrieval plus parameter-efficient fine-tuning and prompt optimization, validated through internal user testing and metrics, cutting manual lookup time by ~40%. Also has hands-on experience orchestrating automated ML pipelines with Apache Airflow.”