Vetted scikit-learn Professionals

“Built and deployed production LLM-powered document summarization/classification systems using embeddings, vector databases (RAG-style retrieval), and automated evaluation (BERTScore/ROUGE), with a focus on monitoring and scalable cloud pipelines. Also partnered with a fraud analytics team to deliver a transaction anomaly detection solution, translating model outputs into Power BI dashboards and actionable KPIs while iterating on thresholds and alerts based on stakeholder feedback.”

“ML practitioner with hands-on experience adapting diffusion models (DDPM + U-Net in PyTorch) to improve low-dose CT medical imaging quality via denoising and upsampling against high-dose ground truth. Also built a RAG workflow during a recent internship by cleaning client survey data, embedding with OpenAI text-embedding-3-large, and indexing in Pinecone with MD5 deduplication, alongside a strong emphasis on production-grade Python practices.”

“Product-focused full-stack engineer (70/30 app vs infra) with Accenture experience and recent AI workflow work, shipping end-to-end systems from React/TypeScript UIs through FastAPI backends to Postgres. Built an AI-driven data extraction platform with async job APIs, strict schema validation, and strong observability, and has operated AWS ECS-based deployments with real incident mitigation (DB connection exhaustion/latency under traffic spikes).”

“Healthcare data/ML practitioner with experience at UnitedHealth Group building production ETL and streaming pipelines (Python, BigQuery, Kafka) that unify EHR, IoT device, and lab data for patient risk prediction. Also implemented embedding-based semantic search/linking for noisy clinical notes via domain adaptation and rigorous validation with clinical stakeholders; previously built churn prediction at DirecTV using XGBoost.”

“LLM/GenAI engineer with US Bank experience building a production financial-document intelligence platform using LangChain/LangGraph, GPT-4, and Amazon OpenSearch. Delivered a RAG-based assistant for compliance/audit teams with grounded, cited answers, focusing on reducing hallucinations and latency, and deployed securely on AWS (SageMaker/EKS) with CI/CD and evaluation tooling (LangSmith, RAGAS).”

“At HPE, led and deployed an enterprise-grade LLM document intelligence platform for an insurance client, automating extraction from highly variable PDFs/scans/emails and raising field accuracy from 74% to 93%. Built a LangChain/Pinecone/OpenSearch RAG framework to cut hallucinations by 37% and operationalized LangSmith evals in CI, driving a 41% triage accuracy lift and >33% fewer incorrect resolutions while partnering closely with claims operations via HITL workflows.”

“Built and shipped LLM/RAG systems in finance and startup settings, including a Goldman Sachs document intelligence platform that indexed ~8TB of regulatory filings and delivered cited, conversational answers with <2s latency—cutting compliance research by ~4.5 hours per batch. Also developed LangChain-based agent workflows at Finta to automate CRM enrichment and investor lookup with strong testing, tracing (LangSmith), privacy guardrails, and auditability.”

“Currently working in CoRAL Lab, built and deployed IntegrityShield—a document-layer PDF watermarking system that keeps assessments visually identical while disrupting LLM-based solving; validated in a real classroom where it helped catch 12 AI-cheating cases. Also built MALDOC, a modular red-teaming platform for document-processing AI agents using LangGraph to run reproducible, deterministic adversarial trials across OCR/text/vision routes.”

“Built an autonomous decision analytics LLM agent for end-to-end tabular binary classification, using RAG (FAISS) to retain context across multi-step queries. Deployed as a FastAPI service with production-style reliability features (schema-aware validation, fallbacks, retries, structured outputs) plus offline/online evaluation and monitoring to reduce analysis time and improve consistency versus stateless approaches.”

“Data engineer with experience across healthcare and geospatial risk systems, owning end-to-end pipelines from ingestion through serving on AWS/Azure stacks. Built HIPAA-compliant data quality gates and CDC for millions of daily claims, and also delivered a real-time wildfire risk platform with 20-minute refresh cycles and a 60% data accuracy lift. Strong in streaming (Kafka), Spark performance tuning, and production-grade orchestration/CI/CD (Airflow, Docker, Jenkins, GitHub Actions, Terraform).”

Anthony Ngumah

Screened

Junior Embedded Software Engineer specializing in robotics, firmware, and AI-enabled systems

Boston, MA4y exp

“Robotics-focused engineer with co-op experience building and debugging embedded C++/Python drivers for time-of-flight sensing on a Flex Stacker product, plus automation of large-scale test data collection via Google Drive/Sheets APIs to enable parallel robot testing. Also has ROS2 sensor-driver experience (GPS/RTK/IMU with custom messages/ROSbags) and is building a side project integrating Whisper-based live transcription with chunked abstractive summarization in a latency-aware pipeline.”

Rushir Bhavsar

Screened

Intern AI/ML Engineer specializing in LLMs, MLOps, and distributed training

1y exp

“Founding AI engineer (June 2024) at Talon Labs who built and productionized an LLM-powered chatbot for interacting with proprietary supply-chain documents, deployed at large scale (25–100,000 users). Experienced with RAG/LLM orchestration (LangChain, LlamaIndex, Groq AI) and production ops tooling (Kubernetes, Docker, Kubeflow, Airflow), with a metrics-driven approach to evaluation, observability, and stakeholder alignment.”

Chandra Shekar Akkandra

Screened

Mid-level AI/ML Engineer specializing in fraud detection and risk analytics in Financial Services

Newark, CA5y exp

“Finance-domain ML/LLM engineer who has shipped production systems including a RAG-based financial insights assistant with a custom post-generation validation layer that verifies atomic claims against retrieved source text to prevent hallucinations in compliance-critical workflows. Also built large-scale MLOps automation on AWS using Kubeflow + MLflow + CI/CD for fraud detection and credit risk models processing 500M+ transactions/day with a 99.99% uptime goal, and partnered closely with JP Morgan risk/compliance stakeholders on NLP-driven compliance monitoring.”

Ankita A Khartmol

Screened

Junior Backend Software Engineer specializing in conversational AI and cloud APIs

Bangalore, India1y exp

“Backend/ML-focused software engineer who built and evolved a Python/FastAPI backend for a large-scale conversational AI platform, decoupling API and inference services to improve stability and deployment velocity. Experienced in production hardening (timeouts/fallbacks/monitoring), secure multi-tenant systems (JWT/RBAC/RLS), and low-risk migrations using shadow deployments and incremental traffic ramp-ups.”

Soham Patil

Screened

Junior Cloud & AI/ML Engineer specializing in AWS GovCloud and MLOps

Washington, DC2y exp

“Robotics software engineer with hands-on ROS 2 autonomy experience on an obstacle-avoiding quadrotor (ROS 2 + Gazebo + PX4 + Nav2/SLAM), including custom work to extend Nav2 into a 3D aerial domain and output PX4 trajectory setpoints. Also built cost-saving ML infrastructure (PostgreSQL + AWS data-cleaning pipeline) and improved object detection accuracy by 40% using CUDA/PyTorch, with strong containerization and CI/CD practices (Docker + Kubernetes, aggressive version pinning) to prevent environment drift.”

Abdul Mohammed

Screened

Mid-level Data Analyst specializing in healthcare and financial analytics

USA3y exp

“Built and productionized an LLM-powered clinical documentation and insights pipeline at Cardinal Health using LangChain + GPT-4 with RAG to summarize long clinical notes, extract medication/dosage entities, and generate structured SQL-ready outputs for downstream analytics. Emphasizes clinical reliability via labeled benchmarking (precision/recall/F1), shadow deployments, clinician human-in-the-loop review, and ongoing monitoring/orchestration with Airflow, Lambda, S3, Postgres, and Power BI.”

Arya Mane

Screened

Junior Full-Stack & AI/ML Engineer specializing in LLMs and multimodal document processing

Dallas, Texas1y exp

“Built a production RAG-based NBA player scouting assistant that embeds player profiles into FAISS, orchestrates retrieval and LLM recommendations with LangChain, and surfaces results via embedded Tableau dashboards. Demonstrates strong focus on evaluation/monitoring (batch tests, LLM-as-judge, latency/failure/token metrics) and has experience translating non-technical founder goals into DAPT + fine-tuning plans on curated data.”

Sana Khan

Screened

Mid-level AI/ML Engineer specializing in MLOps, LLMs, and real-time inference in FinTech

Oklahoma, USA4y exp

“ML/LLM engineer who has deployed a production LLM-powered assistant for intent classification and query routing (order recommendation/support deflection), combining BERT fine-tuning with an embedding-based retrieval layer and optimizing for low-latency inference. Experienced with end-to-end reliability practices—Airflow-orchestrated ETL, data validation/alerting, MLflow experiment tracking, and iterative improvements driven by user feedback and monitoring.”

Aniket Janrao

Screened

Junior Data Scientist specializing in healthcare ML and clinical NLP/LLMs

Houma, LA2y exp

“Healthcare-focused LLM engineer who has built two production clinical applications: an automated structured clinical report generator from physician-patient conversations and a RAG-based chatbot for retrieving patient history (procedures, allergies, etc.). Demonstrates strong applied RAG expertise (overlapping chunking, entity dependency graphs, temporal filtering, graph RAG) to reduce hallucinations/omissions and partners closely with clinicians to automate hospital workflows.”

“Hands-on implementation-focused candidate with experience owning cloud deployments and putting LLM/RAG workflows into production. They stand out for combining customer-facing deployment ownership with practical AI systems work, including retrieval tuning, hallucination mitigation, production incident response, and document-processing pipelines for messy real-world inputs.”

“Built and deployed a GenAI-powered PhD career intelligence platform at NYU that maps academic backgrounds to career paths and converts long academic CVs into job-ready resumes. Stands out for treating LLM systems as structured production pipelines—combining NLP extraction, embeddings, orchestration, and AWS deployment—to improve recommendation quality and cut resume preparation time by 70%.”

Dhriti Kanchan

Screened

Mid-level Data Analyst specializing in healthcare and financial analytics

Texas, USA5y exp

“Analytics-focused candidate with hands-on experience turning messy CRM, e-commerce, payments, and support data into trusted reporting datasets using SQL and Python. They have owned end-to-end churn and retention analytics work, including RFM-based segmentation, dashboard delivery, and metric standardization across sales, marketing, and finance.”

“Built and deployed a production RAG-based internal knowledge assistant that let analysts query company documents in natural language, using LangChain/LangGraph with Pinecone and a FastAPI service for integration. Emphasizes reliability in production through hallucination mitigation (retrieval tuning + prompt guardrails) and measurable evaluation/monitoring (accuracy, latency, task completion, hallucination rate), iterating based on user feedback.”