Vetted LoRA Professionals

“Robotics/AI candidate who built an AI-driven landmark location tool during a summer internship at Mobile Drive, combining YOLOv5 object detection with OpenStreetMap-based geolocation to handle dense, cluttered urban environments. Also researched deploying LLM-based agents on constrained hardware using quantization plus LoRA/continuous learning, improving accuracy from ~80% to ~92%, with an emphasis on production logging for reliability.”

“At CVS Health, the candidate productionized a RAG-based LLM solution in a regulated healthcare setting, emphasizing reliable data pipelines, LoRA fine-tuning, monitoring, safety guardrails, and A/B testing. They have hands-on experience troubleshooting real-time RAG failures (e.g., chunking/embedding issues) and regularly lead developer-focused demos/workshops while translating technical architecture into business value for stakeholders.”

Ishaan Umesh Mandliya

Screened

Mid-Level Full-Stack Software Engineer specializing in AI/ML and cloud-native systems

Los Angeles, CA3y exp

“At BondiTech, built and deployed customer-facing backend improvements for enterprise dashboards handling 1M+ records, redesigning a .NET/Entity Framework API with server-side pagination/filtering and feature-flagged rollout to cut latency from ~15s to ~2s. Experienced integrating customer systems into existing APIs, including stabilizing a legacy CRM sync by normalizing inconsistent IDs, handling strict rate limits with batching, and adding DLQs plus reconciliation reporting.”

Sai Venkata Sathwik Golla

Screened

Mid-level Backend & Applied ML Engineer specializing in LLM systems and scalable APIs

Palo Alto, CA3y exp

“Backend engineer who significantly evolved an internal analytics/reporting platform (Python API + Postgres) powering self-service dashboards for product/business teams, focusing on reliability under heavy concurrent load and fast query performance. Demonstrates strong production engineering practices across API design (FastAPI), observability, incremental rollouts with feature flags, and data security using JWT/RBAC plus Postgres row-level security.”

“ML/AI engineer with hands-on ownership of production recommendation and RAG systems at Northern Trust. They combine transformer modeling, latency optimization, cloud deployment, and monitoring with measurable business impact, including 14% accuracy gains, 12% engagement improvement, and 19% better query relevance.”

“Early-career/new-grad candidate who built TrendScout AI, an evidence-first market intelligence agent that ingests messy news, extracts entities/events, builds a Neo4j knowledge graph, and answers questions via RAG with citations. Achieved ~95% retrieval relevance by combining ChromaDB semantic search with graph-based retrieval and validating outputs through human evaluation and guardrails to prevent hallucinations.”

“LLM/ML engineer who has shipped production systems in legal/financial-risk domains at Wolters Kluwer, including a hybrid OCR+deterministic+LLM extraction pipeline that structured UCC filings at massive scale and drove $6M+ in revenue. Also built LangGraph-based multi-agent “Deep Research” workflows with model routing, tool calls (MCP), persistence, and human-in-the-loop review, and partnered closely with policy writers to deliver LLM summarization that cut writing time by ~60%.”

“Built production LLM/RAG platforms for financial services to enable natural-language Q&A over large policy/compliance document sets stored in Snowflake and SharePoint. Strong in MLOps and orchestration (Airflow, ADF, Step Functions, MLflow) and in solving real production issues like stale embeddings and model performance, including an incremental Snowflake Streams sync that cut processing time from hours to minutes.”

“Built a production multi-agent LLM system at Live Nation on Databricks (LangGraph/LangChain) that let venue/event teams ask questions in Slack, auto-generated optimized route schedules, and produced inventory/stocking recommendations from historical SQL data and venue trends. Improved reliability by tightening prompts with strict JSON schemas, providing sample questions/SQL, and adding guardrails plus synthetic/edge-case testing, while iterating with event managers and senior VPs via prototypes and feedback loops.”

“Data/ML practitioner with experience at UnitedHealth Group building a fraud claims detection solution combining structured claims data and unstructured notes, validated with compliance stakeholders to improve actionable accuracy. Also applied embeddings, vector databases, and fine-tuned language models in a Bank of America capstone to detect threats/anomalies in financial documents, with production-minded Python ETL workflows using Airflow.”

“Built and productionized an LLM-powered support/knowledge pipeline using embeddings and retrieval (RAG) to deliver more grounded, higher-quality responses while reducing manual effort. Focused on real-world reliability and performance—adding structured validation/guardrails, optimizing vector search and context size for latency/scale, and monitoring failure patterns in production. Experienced with orchestration via LangChain for LLM workflows and Airflow for production data/ML pipelines, and iterates closely with operations stakeholders through demos and feedback.”

Sai Rahul Dasari

Screened

Mid-level AI/ML Engineer specializing in Generative AI, NLP, and healthcare RAG systems

USA3y exp

“Built and deployed a production clinical claim validation RAG system at GE HealthCare that automated nurses’ patient-history/claims checks, cutting manual review time by ~65%. Designed the full stack (retrieval, embeddings, Pinecone, prompt/verification guardrails, FastAPI backend) with PHI-compliant anonymization via NER and orchestrated pipelines using Airflow, Azure ML Pipelines, and MLflow with drift monitoring.”

“Analytics professional with Intuit experience spanning modern data stack work, behavioral segmentation, and applied AI. They built dbt/Snowflake pipelines powering retention and churn dashboards, automated feedback classification with OpenAI/LangChain, and partnered closely with product and marketing teams to turn analytics into onboarding, targeting, and lifecycle messaging decisions.”

Shashwat Negi

Screened

Mid-level Software Engineer specializing in AI/ML and full-stack systems

San Jose, CA3y exp

“Data Scientist (2–3 years) at ZS Associates who has built and productionized agentic LLM systems, including a LangGraph-based multi-LLM prompt-optimization pipeline for entity extraction deployed as a Spring Boot microservice via Jenkins. Also built an Insightmate.ai chatbot and improved its RAG accuracy by diagnosing vector retrieval issues and implementing HyDE query expansion, while partnering with sales and pharma stakeholders to drive adoption (e.g., Zimmer Biomet platform migration into a multi-year partnership).”

“ML/AI engineer with hands-on experience building production MLOps systems for predictive maintenance and demand forecasting, including deployment, monitoring, and iterative retraining. Also shipped a RAG-based employee onboarding chatbot integrated with ServiceNow APIs and reports business impact of roughly $300k/month in reduced stockout and overstock costs.”

“Graduate student who has built both full-stack web products and an offline Android AI assistant for visually impaired users. Particularly interesting for roles spanning product engineering and applied AI, with hands-on experience in on-device ML, privacy-sensitive deployment, and making complex AI interactions usable for non-technical users.”